PhD – JosePhD

It is Finished.

The PhD rewires you… now I’m rewiring back. The last lesson of the PhD isn’t in the thesis. It’s learning how to stop mistaking endurance for balance.

It is finished. Two months ago today, exactly three years from when I first stumbled into this PhD life, as the clock struck midnight, I had fully drafted my thesis and booked my viva for two months later. Last night, I hit submit.

The only thing left on my PhD agenda now is to lie down like a salted fish and do nothing. (Yes, a nod to Chow’s immortal line: 「做人如果冇夢想，同條鹹魚有乜嘢分別呀？」— “If a person has no dreams, what’s the difference from a salted fish?”). Well, my dream at this point is to nap, so I guess I’ve come full circle.

Amazing art piece we bought in Lisbon – Fish drawn on broken tiles

PhD changes you (arguably – without consent…)

Doing a PhD is a huge undertaking. You think you’re just signing up for a degree, a structured thing with deadlines and milestones. But somewhere along the way it slips under your skin and rewires you. You start to realise it’s not just about producing a thesis — it’s about quietly reshaping how you think, how you spend your time, how you relate to others.

There are the obvious things: long hours, early mornings that bleed into late nights, bending time around deadlines like you’re running your own private Olympics. Dreams about work — not the metaphorical kind, but actual dreams where datasets, arguments, and half-written paragraphs chase you around.

I did try to protect myself. Weekends were sacred — my one immovable boundary. But the weekdays? They became a kind of over-compensation marathon. “If I keep weekends safe, I should pay it back by working even harder midweek.” And so I did. For the last year especially, that rhythm hardened: fewer dinners with friends, more nights staring at a half-eaten meal with my laptop still open, declining church commitments, community projects quietly pushed aside.

And yet, people who know me would probably describe me as a bit of an octopus — always dipping my tentacles into multiple things at once. Even during the PhD, that was my counter-measure: to resist being swallowed whole by the project, or by the endless temptation to improve methodologies on an upward-to-infinity scale. It was my way of clinging to variety, even if just at the edges.

At the end of the day, however, these are futile attempts to move an immovable load of work. All these cost sneaks up on you. No energy left to develop hobbies. No mental space to nurture relationships properly. No margin for anything that didn’t have the word “PhD” stamped on it. Life became PhD-centred, and everything else rotated around it like satellites around a heavy, slightly cranky planet.

Once the dust settled, the obvious question came knocking: is this how I want to live?

The last lesson

The final lesson of the PhD isn’t in the thesis. To me, it’s this: how to unlearn those patterns. How to stop mistaking endurance for balance. I’ve trained myself to push through – (to a point it becomes more enjoyable to just stay) — to add just one more paragraph, one more figure, one more revision. That survival mode worked for the PhD, but it’s a terrible blueprint for life. Productivity becomes its own trap: there’s always another article to read, another method to refine, another dataset to clean. The horizon keeps moving, and so do you, until you realise you’ve forgotten how to stop.

So the work now is almost the opposite: learning the discipline of switching off. Letting “good enough” actually be good enough. Accepting that leaving something unfinished until tomorrow doesn’t mean failure; it means you still get to have a tomorrow. Funnily enough, most of my codes do run much better the next day – once I had a cleared out mind to disentangle what kind of crap I scrambled together the night before.

My attempt to self-correct include:

Leave the office no later than 6:30pm.
Work from home a bit more (countering my tendency to loiter at the desk just because I can).
Say “yes” to new things outside of work. For example: training for a half-marathon. Current progress: minimal. (Help me out, runners of the world).

Onwards

I’ll save the spiel about “thesis format vs publication” for another time. For now, I just want to say I am eternally grateful to my supervisors, family, and friends for putting up with PhD-ing Jo. The PhD has taught me to get better at work. Now I need to continue to get better at life. The bettering doesn’t stop here. Here’s to a fuller, more balanced, more experienced life ahead.

The Elevator Problem

Ever frustrated about waiting for elevators and thinking there must be a better arrangement of default positions to minimise waiting time? Here’s an app I wrote you can try out! I wrote this with UCL Institute of Child Health in mind – so a 5-storey high building. Use the website for best experience.

You can customise:

number of elevators
elevator speed
door operation time
distribution of destinations, and call volume for:
morning-peak;
evening-peak; and,
non-peak hours

https://ichelevatorsimulation.streamlit.app?embed=true

With my simulations, the best configuration with default setting + elevator speed = 4.0, door operation time = 0.2 minute per stop:

Morning-Peak: with avg. wait of 0.54 minutes.
1 lift on ground floor
1 lift on 3rd floor

Evening-Peak: with avg. wait of 0.24 minutes.
1 lift on ground floor
1 lift on 4th floor
(Assuming half the calls for elevator per hour than morning peak)

Non-Peak: with avg. wait of 0.14 minutes.
1 lift on 2nd floor
1 lift on 3rd floor
(Assuming even fewer calls per hour)

Of course – I still need to update my assumptions on number of calls, elevator speed, and distribution of journeys. And there are quite a few other things I did not consider in this simulation, such as multiple stops.

I think a model like this reveals insight I don’t directly think of, such as, when elevator speed is fast enough, it does not really matter what configuration of default is set; or, when call load is really high, the small amount of operation time add up exponentially.

Nerdiness and jokes aside, I suppose this would be a good exercise/tool to communicate research findings to the public – but creating these interactive models with immediate feedback – to justify decision-making, resource distribution, and the many different stakeholders & things one need to balance.

It has been a fun little practice!

Evolving Language in Healthcare: Empowering Patients or Consumers?

Language around healthcare provision has been changing over the last few decades, with a stronger emphasis on people’s role and capacity in making their decisions on the care options they wish to receive. This shift is motivated by the intent to balance the power dynamics between healthcare professionals and people who receive care, with the hope that this would reduce risk of harm/iatrogenic events.

Studies such as this examined people’s different preferences (in a mental health service context), and they all come with a reason – even for the less popular term “Survivor”, as the term appears to be describing their struggle to get relevant help in the system. The term “service user” is being increasingly used, within and beyond healthcare, in other domains such as social care or learning disabilities, where the people’s care needs is not necessarily seen as an illness. There is no good recommendation on which term should be used across all circumstances in people-facing materials.

Let’s take a step back: What the language is hoping to achieve, is to safeguard the relationship between healthcare professionals and people who receive care, such that individuals’ health is maximised. Language is relational. The study above highlighted the same thing: communicate and find out for whom which term is best used, and build this carer-cared relationship with respect.

Respect
Photo by Cytonn Photography on Pexels.com

My biggest qualm with the term “service user” is based on exactly this: how the term (potentially) changes the patient-doctor relationship into a consumer-provider relationship. I think the consumer-provider relationship comes with 2 major flawed assumptions:

assumption 1: All problems could be fixed, it is only a matter of price.
This thinking embeds deeply in capitalism that there is always a better solution to the current problem, over-estimating the capabilities of human knowledge on medicine.

assumption 2: All service providers are incapable – there is always someone else who can provide better services – reliance on competition. This is the other end of removing power from healthcare professionals. This removal of respect to professionalism changes people’s attitudes to help-seeking. People now ask for personal recommendations “Which doctor do you know is the best?” – instead of believing in the standards of which the profession is established on. The push to physician associates rides the same tide of the removal of professional standards of medical professionals.

Help-seeking is now dependent on social capital; better service is now linked with cost; the spirit of national healthcare service where health is a basic right is challenged – being healthy becomes a commodity. Inequalities, along with a thriving private healthcare service, will be widened as a result. The lack of a trusting relationship, and move to a transactional relationship between healthcare professionals and people also mean that the end goal of health service provision is no longer focused on individual functioning with alleviated illnesses, but patient satisfaction, which with the false assumption of what level of health is possibly attainable (in certain timeframe), could be impossible to satisfy. This fuels derogatory attitudes towards healthcare professionals, nurses, healthcare assistants, and doctors. No wonder retention rates of hospital staff remains low.

The same phenomenon and patterns are clear in other domains like education, and with the fast development in the capabilities of large language models and artificial intelligence, expertise is becoming less and less valued. Existing potentially trusting relationships between professionals and people need to be reforged and reimagined – and it all has to start with listening and involving people with lived experience in the generation of knowledge and expertise.

Existing **trusting** relationships between professionals and people need to be reimagined
Photo by cottonbro studio on Pexels.com

Culture changes, attitudes changes, patient may be a term running out-of-date, and service user may be no longer in favour. Healthcare provision has to capture, value and reward the building of trusting relationships, and have a longer-term vision in investing in community building. This is bigger than healthcare planning, but on the relationship between the people and their government and policy makers. The people are watching.

Conference Thoughts & Reflections

It’s been a whopping 2.5 weeks of conferences, presentations, exploration and learning in September for me. Kicking off at the Royal Statistical Society International Conference (RSS) in early September (2 presentations), to a week at Institute of Health Metrics and Evaluation (IHME, University of Washington, Seattle) (2 invited lectures), followed by a week-long conferences and workshops at the International Population Data Linkage Network Conference (IPDLN) at Chicago (1 poster, 2 presentations). Lots of “firsts” this month, and this is a blog to organise my thoughts and experiences!

RSS – 4-5th September

Presenting at RSS, article will be out in November 2024 Edition of Significance!

My first ever RSS conference and I wish I had learned about it sooner! The conference is a great mix of statistical theory, methods and applied research, and an international representation with >700 attendees from academia, media, and people from government statistics. A flurry of exceptional talks and keynotes started with Tim Harford, reaffirming that it is key to reject the notion that “Stats can tell you anything” because it becomes synonymous to “Stats can tell you nothing” – undermining the confidence of the public (and sometimes our own!) towards statistics. Pivotal to my theoretical thinking & development is Erica Thompson’s talk and book “Escape from Model Land”, encapsulating the need for modellers to make use of their expert knowledge in their interpretation of statistical models, and take accountability, instead of pretending there is strong objectivity in research. My talk as the winner of the RSS Early Career Writing Competition was well received, with several follow-up chats and emails. It’s another imposter syndrome moment – who am I to give a talk to these brilliant and renowned statisticians!? People I met at the conference were kind and open to conversations, and I felt very welcomed from start to finish. Statisticians are a special group of people!

IHME – Seattle, University of Washington

“Stay open to new data and be prepared to keep freshening up your knowledge”
Hans Rosling, IHME

First few steps into the building and was greeted with Hans Rosling’s quote “Stay open to new data and be prepared to keep freshening up your knowledge.” Another humbling experience giving a 1-hour seminar, titled: The Road to Racial Health Equity Starts with Data Equity, Transparency and Clarity, with a strong 80+ audience online, and 10 in the room. Again I fell into my typical long-winded mode and couldn’t finish all of my slides, but it is re-assuring knowing that what I have been working on in the last 2 years can be meaningful and impactful to how researchers approaches racial and ethnic health inequalities. The dynamics at IHME is always upbeat with biweekly goal settings and meetings. As a PhD student, my research is sometimes a bit isolated from the rest of the group. It is quite nice to re-immerse in a team-based research process for a bit! I was ecstatic to visit the paediatric residents on the Health Equity Track at Seattle Children Hospital, had great conversations about quantitative and qualitative approaches to improving health equity. Also inspired my initiative to share our stories!

Conversation with Paediatrics Residents on the Health Equity Track, Seattle Children Hospital

IPDLN 2024, Chicago

Chicago – it’s been over a decade since I last visited and it is ever-stunning. IPDLN Edinburgh was my first conference as part of my PhD, I was 3 months in, and was properly amazed by the breadth and depth of topics discussed then. I hoped I could one day have an opinion, have something valuable to add to this community – and I did! With new friends I met at IHME, I was invited to help out at a pre-conference workshop, presented a poster with international collaborators (and meeting some for the first time in person!), and gave 2 different presentations. I am so glad to have met people across UK, US, AUS, NZ and beyond, working in inter-related areas on record linkage. Incredible workshop on data visualisation by Rowena from Wales, and again featuring Hans Rosling on his BBC visualisation on life expectancy! His legacy is felt deeply in this trip, and every time we hear from colleagues working with Swedish data! I look forward to keep being a part of this evolving community!

L to R: Peter Christen, Rainer Schnell, Joseph Lam;
Presenting work led by Sumayya Ziyad who cannot make it to Chicago this time.

Having Fun

I have yet to talk about the entertainment and cultural exchange bit 🙂 Seattle and Chicago has treated me well, from Space Needle to the Bean, from underground walk to architectural boat trip, from jazz club to blues bar, from Burke Museum to Second City, from Seahawks to the Cubs! This will be undoubtedly a fond memory when I finish my PhD – final stretch, now back to work!

Temptation of Treating Science as Sovereignty

Science as Sovereignty, and the problem of this.

As Nietzsche shared his observation that “God is dead”, as we enter the (post)secular world, we seemingly departed from the need of revelation from supreme beings to ascertain the truth. However, Schmitt believed that political sovereignty remains (re)enchanted within the same theological framework, that modern national sovereignty is a reflection, or a derivative of Theo-sovereignty. In Chan’s book, he attempts to disenchant the Theo-political relationships in this atheistic age.

I cannot help but project the arguments & discussions in the book to my line of work: in health research and policy making, that Science is disguised to fill the gap of Theo-Sovereignty.

“Follow the science” – Not sure if anyone kept a count of how many times Boris Johnson and his crew repeated this phrase during the pandemic. They certainly walk their talk in at least 2 dimensions: party gate, and in policy making – slow & weak masking recommendations. Nevertheless, “follow the science” stirred up a lot of emotions and debates within the country – arm-chair epidemiologists, experts with polar opposite recommendations, fake news… Amongst the fury of opinions, the government framed science as the rightful king, filling the gap of Theo-sovereignty, as a voice of a justified and absolute authority.

A character of sovereignty is that it is above the law, that the sovereignty shapes the law, and the law serves the sovereignty. Social distancing, no home visits, masking… In this way “science” very well fits this description, determining “What” a society should do. Anything that strays away from “Science” deserves mockery and rejection, if not punishment and persecution.

All sounds good, only if science is as straight forward as a binary yes or no. In my view, science is an art of embracing and interrogating certainty. “It depends.” Science by nature cannot act as a sovereign power to rule a society. Hence what we observed in the UK (and many other places), is instead a colluded form of scientific sovereignty, a nationalism-biased evidence-picked aristocracy. This camouflage of nationalism in science is most evident in global (health) research. In the following paragraph, I wish to demonstrate how current approaches in defining “Who” should do research is colluded with nationalism (national sovereignty).

There is an increasingly popular narrative in academia that global research should be led by people from their corresponding country. Not to be mistaken, I am fully supportive of the said initiatives, providing funding and opportunities for scholarship to develop in the global south. I’ve gone out of my way to support researchers from overseas to learn research methods, statistical software, and job interview preparations.

However, this narrative is often reduced into a means of reattribution or reimbursement of the western colonial past. This emphasises on appearance and country of origin takes away from the researchers’ abilities, but more importantly, feeds into the increasingly prominent nationalist agenda: “it is their history, let them research it”, “it is their people, let them help them”, in short, “it is none of our business.” By no surprise, as the UK economy showed signs of slowing, the overseas aid (which is used to support a lot of global research) was immediately cut. This new “scientific” post-Theo-sovereignty is not gentle nor kind, but self-preserving, self-promoting and self-indulging.

Science does not happen in a vacuum. But it if does, we would easily reach the conclusion that researchers from the local countries are more likely to understand the context better, which makes research, communication, implementation and much more easier. The preferential development of scholars from the global south to solve local problems makes sense. These initiatives need not to be framed as promoting diversity, which could be twisted into a form of exclusivity, but simply as a better approach to produce better science.

Why hasn’t in the past global south scholarship taken a larger role in academic research and policy making? Why hasn’t they be considered suitable candidates? Our measurement of capability is always framed by a limited few. Did you graduate from Oxbridge? Did your parents work in academia? Did you publish from x y z journals? It is the university application season soon, have a look at the plethora of university league tables floundering themselves to their potential (mainly overseas) customers, aren’t we all playing by this game a greater authority designed, as they saw that this was good?

We are enchanted by the belief of certainty “Science” provides. This is no fault of the scientific approaches, but the collusion of other worldly powers (e.g., nationalism) that biases how progress is achieved and measured.

This is usually the place I offer a solution, some insight or revelation. But I really don’t have one. As a Christian, I believe in God’s provisions big and small. Researcher is a role equally as impactful to improving people’s lives as any. If God forbids, I shall do as much as I can as a researcher. My life and mission is bigger than this role. And I hope you find yours too.

This blog collects my early reflection from reading Dr Chan Ka Fu’s new book on political theology. I’m still very early on in this book, so I might have misrepresented Dr Chan, if so I apologise!

Open (Data) Science – Data Management is Key

Till the end of the day, it is rare for any finding to be considered completely irrefutable or beyond scrutiny.

Psychology degrees do give you superpowers. No, you can’t read minds, but you can shut your eyes and perform t-tests on SPSS. There is a point in time where I can rely on muscle memory alone to get the p-values popping up my screen. I can see how for simple analysis point and click solutions can be much preferable.

It wasn’t until my final year project that I started to really recognise the importance of transparent and replicable codes and research processes. I had to co-collect data along with my peers, we each collect half of the data. The data collection process is by no means easy: a collage of cognitive tests, long structured interviews etc. keeping the participants (mainly other undergraduate students) engaged and put effort was always a challenge.

All is well until I discovered my data collection partner was popping in random numbers in the dataset. “What is she doing?!” Shocked I was seeing this, but even more shocked when it turned out that she forgot to record age and gender in the interviews. Not having gender is less relevant, but age is a key variable we have to take into account for the distribution of IQ differs by age. I was quite disappointed as this meant that the data was compromised, and I couldn’t bring myself to really trust what the data could tell me – if basic demographics are made up, how credible can other information be?

I was annoyed, just like this cat.
Photo by Anna Shvets on Pexels.com

I think of this experience often as I read research papers that does not describe their data collection and analysis plan very well. In academia, more people are willing to share the codes they used for analysis. I believe the next step is to extend the transparency to data cleaning and management processes. When describing the process of data curation, it is easy to focus on describing the psychometric properties of the questions. However, I believe there are a lot of wisdom research groups can benefit from sharing their responses to more general questions like: How is the database managed? Were there any challenges to data management, how were they resolved? Studies and initaitives like PROSPER (PROfeSsionnal develoPmEnt for Research methodologists), helps consult and formulate future developments plans for research methodologists. I am happy to see how things are developing, and for methodologists to finally gain the limelight a bit more in research!

Till the end of the day, it is rare for any finding to be considered completely irrefutable or beyond scrutiny. The best we can do as researcher, in my opinion, is for future researchers to acknowledge as they read our findings, and say: “They were bound by the knowledge of their time, that was the most rigorous way they could have done it!”.

A key development goal from my PhD is the ability to develop codes and wrangling with data across Stata, R and Python. I am still learning the way to work across platforms that would make the most sense! Do share with me if you have any tips 🙂

Chapter 5 – One (Only) Way forward.

I propose that only via shared understanding, relationship-building, a community that preserves space for authenticity and solidarity can truly achieve meaningful representation – with unity in diversity.

The concept of “Global Village” was born in 1964 by Marshall McLuhan, who illustrated how the technical advancements in communication abolishes geographical and temporal boundaries. There emerged an innocent globalist and cosmopolitanism view that harmony in diversity will be achievable in no time. The Culture War narrative took over, as the after-(ongoing) effect of the economy crisis ripples, as the “compete for survival” instincts kicks back in economic and cultural terms, the harmony in diversity fantasy seems much more far away than it was in the 1960s.

In earlier chapters, I criticised HEI’s approaches in promoting representation. I argued that the current measures of diversity could easily be portraited as performative, quasi-pro-diversity mandates that drains energy from both the dominant and minoritized groups, as the former feels like they’ve been unjustly underserved, and the latter tokenised, seeing little actual improvements.

How then, can we create and preserve a safe and empowering space that community can thrive, with harmony in diversity? In my opinion, there are 2 key attributes to such communities: Authenticity and Solidarity. And both of these attributes can only be demonstrated through patient, non-judgemental listening and communication.

1. Authenticity

I believe that our identity is continually constructed through our interaction with our circumstances, with respect to history, personal struggles, evolves and adapt to our environment. If we truly respect ethnicity as “self-defined and subjectively meaningful to an individual.” (ONS, see discussion on this in Chapter 3), we have to allow individuals, especially young people in HEI, to have the courage to embrace and explore this uncertainty. We have to reject label-driven classifications to pre-determine how we should interact with others. Here’s my story to illustrate this point:

I attended a language class at the university in London, this was not too far from when the 2019 Hong Kong Democratic Movements have made the news in the West. The first few terms we learn after “What’s your name?”, “Where do you live?”, would be – you guessed it – “Where are you from?”. I am in a small class of 10, coming from different countries, ranging from Switzerland, Germany, Poland to Pakistan, Iran, China. We took turns to ask each another the question – where are you from.

There is a stark contrast in the temperature of the room when I said I am from Hong Kong, and my other classmate said that they are from China. I was welcomed with a lot of warmth, and them a much less welcoming acknowledgement. It is no surprise that the China = Bad overly simplified narrative has crept into the classroom, and affected how we treat others. I felt it, and I decided to share with the class the cultural similarities between Hong Konger and Chinese people. The class was less hostile (yes.) as they now can see my Chinese friend more as a person, and not as an extension of the communist suppressor as they may have previously perceived.

The more socially acceptable, easy thing for me to do in that situation would be simply add salt to injury, to explain how Hong Kong is different from China. I chose not to do that as that would further undermine the class as a safe space for my friend to explore his identity. But this is not just for him. I can easily imagine that this act of differentiation would drive me further away from my dual Hong Konger-Chinese identity. I admire a lot of the elements in Chinese traditional culture, the food, the language, the art… Yet there is strong social pressure for me to denounce part of my identity, and only by doing that my social standing in the environment can be affirmed. Knowingly or unknowingly, my self-identity would change, not as a result of authentic, soul-searching, but under the influence of social correctness or social desirability.

An environment that truly enables authentic identity building need not to be value-free, but it requires individuals to be treated with no presumptions that is based on group identity one may be prescribed as having. It means that individuals have to choose the hard way, to not rely on mnemonic devices of ‘labelling’ too much when we meet and interact with others. This leads on to the second attribute – solidarity.

Languages by Library of Congress is licensed under CC-CC0 1.0

2. Solidarity

A community that endorses solidarity within itself share a key assumption: that every individual in the group is valued as much as the other. There are a lot of discussion on the importance of solidarity so I won’t drill into this too much here.

To highlight how HEI values diversity, a common approach was to collate a long list of cultural or religious events or dates that is happening each month. This was intended to create opportunity for staff and students to demonstrate solidarity with others. I strongly doubt many people read them, or “celebrate with” others. My observation is that, apart from being startled by the sheer amount of “festivals” and “celebrations” that are on the list, the biggest barrier that stops people from “celebrating with”, is the lack of relevance behind those jubilant pictures and exotic foods. I think it is not meaningful to include every festival you can think of purely on the notion of inclusivity. It has to come from the population you intend to share this list with, and it has to be an invitation to “celebrate with” the relevant groups. How do we stand in solidarity if we don’t even know they exist? Representation of non-existing people does not make sense. HEI as a porous community, be it at department, institute, or the University level, must allow individuals to willingly share, and take initiative, and have their skin in the game to allow the above to happen.

Ultimately, the narrative that work and life should be separated, that one’s goal for life is retirement, and that the individuals as just a cog in the system strips people self-worth and sense of community. Under this narrative, your coworkers not worth your time, but another replaceable, disposable piece of work to listen to, understand or build relationship with. But there is no short-cut to diversity and proper representation. There is no laws, rules, or recommended practices that can foster relationship. Authenticity and solidarity needs to be centred at the heart of any diverse community to develop shared understanding. With no understanding, there is no true diversity; with no true diversity, there is no true representation; with no true representation, there is no equity.

An authentic community with individualised understanding and solidarity is key to proper representation, and equity (in the long run).

There is one, only way forward:

“…Thou shalt love thy neighbour as thyself”
Mark 12:31

“There is no panacea, or utopia, there is just love and kindness and trying, amid the chaos, to make things better where we can. And to keep our minds wide, wide open in a world that often wants to close them.”
Matt Haig, “Notes on a Nervous Planet”

End.

Are theories over-rated?

A short reflection based on my observations on trends in mental health research. With audio narration.

Listen to the blog here.

Research methodology 101 in psychology typically starts by explaining statistical hypothesis testing, how data can be understood in a certain way (model) to draw inference. A theory-based statistical model is the approach in which researchers make meaning out of the constellation of data-points – in a systemic and falsifiable way that differentiates inferences from astrology.

Research is not easy. There are many decisions and assumptions researchers make in the process, e.g., how are concepts defined, how are these concepts measured, what are the relationship between these variables, do they overlap? Researchers design, clean, collect and frame data in a way such that they can tell a story – Data may speak for itself, but the theatre is built by the researchers. It is more than choosing which variables to put into the model, or discover which variables are statistically associated with the predictors. It is about how the confirmation or rejection of the statistical model should be interpreted, in what context, for which populations – and more.

Psychology research methods 101 – Hypothesis
Photo by Tara Winstead on Pexels.com

The industrial revolution automated jobs and led to an expansion of productivity; the “artificial intelligence (AI) revolution” appears to share similar aims. The first questions that pop to people’s minds are – “Can we automate this process? If so, how?” The same ideology has been applied to understanding data – there are AI models spring up like mushrooms after rain, with approaches like “covariate auto-selection” that promises to perform as good as (or outperforms) “traditional analysis” – whatever that means.

I am no fan of such practices. This is because I think data analysis is only a small part of the whole scientific process, there are limited ways you can “let the data speak” if the paradigm of data collection, conceptualisation etc. is never challenged. This AI-do-all approach, if deemed to be the best, or even worse, the default practice, will leave little room for users to challenge the premises and assumptions in which the inference are drawn, hence no true empirical theoretical advancements, but post-hoc theory-making. But can you really blame AI data scientist for this?

There is no point in finger-pointing [maybe 1 >:o)]. The problem of weak theory is prevalent in (mental) health research (More discussion here on formal theory: https://eiko-fried.com/on-theory/ – Eiko’s blogs, with a lot of resource on theory, do check them out!). An example that is highly relevant to my work is the use of ethnicity in health research – is it biology? Is it country of origin? Is it migration status? Is it social support and network? What is it’s relation with the covariates? Papers often describe whether their findings fit with previous research, but most of the time stopped at that level, “More research is needed”, and less discussion on theory. It is this tendency of focusing just on inference and less about theory that precipitates AI-based analytical practice to expand.

AI helps make meaning from a pre-specified framework
Photo by Tara Winstead on Pexels.com

This phenomenon begs the question, why is theory playing less of a role in mental health research? What is the driver behind this change in scientific practice? I believe a particular emotion – frustration – plays a role. I see this frustration arise from the huge implementation gap, and the insurmountable unmet needs, which is made worse by the replication crisis.

We are said to be in a mental health crisis. The healthcare system is more sensitive to detect mental health problems: they are recognised earlier and more broadly at primary care, but our ability to treat our patients did not improve to the same extent. It takes 17 years to translate health research into practice. IAPT, new waves of psychotherapy, medications… These attempts to improve service provision (by quantity/access) and quality did not match the increasing demands. With record level of demand for mental health support (even before Covid19), the whole community is pressured to provide solutions. The frustration stems from the compassion to the plight of patients.

The same frustration is felt by the funders too: decades of funding to find a pill to eradicate dementia, pilling resource to prioritise “what works”, stronger than ever appetite for interventions. The positioning of researchers in the field is no longer “neutral observer of (natural) phenomenon”, but “proactive driver of change”. The increasing need to demonstrate “impact” is evident of this change of positioning. Measure of impact depends on ability to demonstrate progress. Theory development is often a twisted journey, it intrinsically fares worse than randomised control trials in that regard in the current paradigm.

In conjunction with the replication crisis, where small sample size and poor methods (but not weak theory) were deemed to be the culprit, strength in numbers feels like a pre-requisite to publish in high-impact journals. This shapes the ecosystem of academia. Bigger institutes are in better position to run larger studies, hence sustenance of the self-prophesised loop of impact as the top research institute. There are less options for smaller institutes to compete – to rely on impact-driven evidence making, rather than theory testing or development. Research became more focused on interventions and local adaptations, rather than trying to come up with a grand theory for a disorder.

Researchers do not have to choose binarily between “theory” and “intervention”, there are plenty of middle-ground between the two. In fact, they go hand in hand to the development of any field. An “intervention”-leaning environment amplifies the need for researchers to understand and clarify “context” – how accumulated evidence can be applied to the situation at hand. I don’t think we are very well trained in this regard (yet), it hasn’t been the focus in the past, nor included in the curriculum. Approaches such as realist evaluation, rapid qualitative reviews etc. arise to address this gap. A “theory”-leaning environment, on the other hand, emphasis on understanding the nature of a phenomenon. For example, the biopsychosocial framework encourages multidisciplinary treatment, which hopefully the restructured integrated care systems are in better position to provide. Another example, where digital based mental health intervention apps taking many different approaches failed to live up to their expectations, perhaps rekindling the positioning and theory of such interventions is the bridge to success. Theory serve as a foundation for knowledge to be generated, decisions justified, and help the field explore alternative explanation of “reality”.

What’s next? It is for us, members of the scientific community to live out the direction of our field. We need to be pragmatic to come up with solutions to address the huge mental health needs, but we need to continue to be observant, patient, and preserve space for new theories and alternative framework of understanding of mental health to be developed and tested.

No Milestones Too Small

celebrating a mini-milestone of my PhD today!

PhD can be a lonely journey – celebration of things big and small can help us recount what we have done so far, and help us put things into perspectives. I am recording this today so that future me will be thankful!

Today I am celebrating that I have finished scrapping/recording number of citation of over 4000 papers!! This serves as part of a bibliographical review that I am doing to understand how ethnicity is described and theorised in literature. I tried to write a small tool in Python (https://josephd.uk/2022/08/11/first-python-tool/) to automate this process, but later decided it is not worth the risk & time to auto-scrap from Google! This meant I will have to do this the old-fashioned way: Manual Searches!

This meant that for the past 3 months I have spent several hours every weekend doing one simple task – ctrl+c, ctrl+v, type number, repeat. Whilst my pinky is aching a bit (from pressing ctrl too hard, and too frequently), there is a strong sense of accomplishment when all 4000+ rows are filled! I split the 4000+ records into batches of 50rows per file, such that I can easily stop & restart whenever I wish. This also helps distract me from the startling size of the task, and allow me to focus on the 50 in front of me.

I found myself spending way more time than expected to complete this, mainly because I was distracted by – you guessed it – the papers themselves! I have yet to decipher what a “perfect” academic paper title should read like, but I am certainly drawn to read quite a bit of them as I copy-and-pasted them. This is very much a blessing in disguise!

The next task is further screening and data extraction from these papers. I hope these findings can be shared with PPIe groups that I am intending to organise (if I get that mini-grant!). Future me, know that you are contributing to something that is meaningful to you – which is the least ambitious, the minimally sufficient motivation for any work!

Officially Enrolled – New beginning

anti-cv of my PhD application history, and some reflections on failure

First term in my Psychology undergraduate course, we were introduced to BF Skinner’s operant conditioning. It relies on a simple premise that behaviours that are rewarded will be reinforced; vice versa, behaviours that are punished will be diminished. Lab mice shall soon learn to jumping through hoops while reciting pi.

“Every failure is a step to success” William Whewell’s motivational speech did not distinct “failures” from “mistakes”. I do think there is a fine line between those 2 – mistakes doesn’t always lead to failures, failures doesn’t require any mistakes [mistakes are neither necessary nor sufficient for failures]. There is a role for uncertainty, for luck, for other unseen circumstances that have led to the (un)desirable outcome. Whilst this should not be exaggerated to an extent that the individual blindly believes a predeterminism that requires luck and nothing else, reserving ones humility and respect for the uncontrollable helps separate ourselves from the lab mice – to not be taken back (too much) by the all too common “punishments” in life.

No different from any other keen beans in my cohort, I started worrying & applying for jobs and PhD half way through my MSc. July 2019 also marks the beginning of my failures of PhD applications. In the 2.5 year window, I have been invited to 10+ final interviews, written 5 full PhD proposals on different topics:

self-harm and apathy
cash-transfer and depression in LMIC
natural language processing in clinical records
Ethnic density and psychiatric illnesses
Universal Credit and mental health problems [data linkage]

I applied to multiple funders such as MRC, ESRC, Wellcome Trust, Alan Turing Institute, NIHR… and many other DTP schemes. Failures after failures. I polished my cv, practiced my interview skills, brush up my twitter profile, present at conferences, write blogs and podcasts… But my “PhD Applications” folder failed to escape their destiny – rename, (rejected). After all the failures, nothing seem to be helping my case. I felt stuck – as my internal locus of control urges me to tackle my “mistakes” to deal with these “failures”. What more can I do? Am I just attending these PhD interviews such that the panel can say the diversity requirements are fulfilled? Perhaps I am just not good enough. The cost of living away from my hometown and family is high, why must I stay in the UK? These are questions I interrogate myself with, as Covid rampages across the world.

…because we know that suffering produces perseverance; perseverance, character; and character, hope.
Romans 5:3-4

I learned to be patient, only because I cannot proceed. I am no any more persevering than anyone. I am privileged to be supported by great colleagues and friends, privileged that my family encourages what I do, privileged that I am passionate about the health and suffering of others, that this passion helps me to be curious, and curiosity brings motivation.

I am grateful for my current duo role as a research assistant and a part-time student. This helps immensely on the financial situation. This is better value than any previous studentships that I have applied! On top of that, I can now appreciate how my previous failures have bought me time to understand academia loads more than when I first graduated. It could be that William Whewell is eventually correct. A mistake is not necessary nor sufficient for us to learn from – a failure can serve the same purpose. Fresh into my role, I have welcomed my first rejected mini-grant application. But now I am much more ready to face it, taste it, and learn from it.

“15th October, 2022 – This is to confirm Joseph Lam is currently enrolled as a MPhil student at UCL.”

To happy learning!