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 point 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 is 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 for 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 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 🙂
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.
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.
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.
There is one, only way forward:
“…Thou shalt love thy neighbour as thyself”
“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.”
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.
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.
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.
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!
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.
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.”
2 factors that affected my motivation at work: some reflections
Started writing this blog on the 3-month mark of my role. Lack of Motivation has very seldom been a problem for me. Transitioning into my current job, I did struggle with this a bit, especially when I am working from home, with an unstable internet connection and accompanied by a novice flute player downstairs.
I am grateful to be able to be inspired by my mentors and colleagues that has allowed me to reflect on how I work. Here are my 2 barriers to motivation.
1. (In)Ability to Contribute
I’ve received absolutely great support from my supervisors and colleagues. I did not feel like I was dropped into a completely new place with no one to seek help from. People are welcoming, I get to meet new people every now and then. Yet there are still times when I have not felt like a part of the group. The working style in my previous role was quite different from my current one. My previous team is rather spontaneous, we are constantly chasing deadlines, constantly speaking and collaborating with each another. My current role is more structured, things are moving in a slower pace, and it is not very often we get to tackle a problem together. This change of pace and team dynamics mean that sometimes I feel out of place.
In a conversation with my mentor, she noticed that I appear to put a lot of my emphasis on my ability to contribute to the team. Upon further reflection, I think that is very true. I am eager to give, but not just to take. But with my limited expertise and knowledge, I felt like I am in no place to give. And I hope this is not based on mindless patriarchal desire for people to listen to me, but to position myself as a valued member of the group and community, instead of a recipient of charity and welfare. My double-identity as a staff and a student also plays into this. I was heading to a blind alley with this train of thought, and it sometimes then stripped my focus away.
My mentor spotted a gap in my way of thinking. Contribution doesn’t always have to be about knowledge and expertise There are all kinds of contributions. Being kind, active on Teams chats, willingness to listen, responsive to emails, sharing my own perspectives and stories, smile… There are many ways how my presence could help make the team better. That helped me remember that me, as a person, has much more to offer than a domain specific knowledge. I care about equality and inclusion, I care about workers’ rights, I am eager to rise people up. These all shall anchor me as a valued member of the community. Motivation follows.
2. Stress to Represent
We talk about gender/ethnic/any representation a lot in our society. Being the “one” in group appears to be magical – it’s the fundamental step of a building fairer world. That is all good.
But there is a, perhaps, unintended consequence that comes with the above narrative. People from minoritised groups are always under a stress to represent. This stress comes from multiple directions:
a) Am I representing my ethnic group well enough? Will my inadequacy hinder my groups’ already small chances to progress in life? There is this constant worry that it is not enough to be just as good as everyone else. One have to do well in every part of life: always dress smart, be professional, don’t make mistakes and stay on the safe side… And that is not always “me”.
b) Is how I am representing “ME” a product of conformity with social expectations of who I should be? Should a HongKong/Chinese person always be good at Maths, a little bit timid in social interactions, be a diligent worker, bad a driving… It is not about the positive or negative conations of these impressions, but rather questioning, is how I was perceived by others truly comes from me, or is it a implicitly implied characteristics that I should have in order to be socially accepted as a person coming from that particular cultural group.
This thought coincides with point (a), if I am not demonstrating characteristics that would fit a public understanding of how Chinese people should behave – and these characteristics could be positively or negatively judged upon, how would then my fellow people be perceived?
Represent. Represent. It is counter-intuitive to think that the burden of representation is laid onto 1 person- no single person could fully represent any group, which is intrinsically a combination and emergent identity that no single person can fully grasp. We often set our EDI recruitment goal at a merely the representation level reflected by descriptive demographics. Yet 1 is miles away from demonstrating diversity WITHIN any given group.
Being a One/few-in-many does shape my self-perception. The process and reflection I describe in (2b) above is dynamic. It could well be that over time, that I become more and more similar to the media-portraited image of a Chinese person. It might not be a bad thing either. But perhaps I am not yet ready to represent this label. Perhaps I need to know myself more before I could allow others to learn about my group, and the difference between the two. It would be much appreciated if this process of self-discovering is not needlessly pressured to accelerate, that I won’t have to force to choose the group I am not ready to represent.
I am grateful for my current workspace, that I have the luxury to think about and reflect on these things that interfered with my performance. Some of these, like (1) could be resolved, but other (2) would require a change in societal attitudes towards in-group, towards others, and towards ourselves. I hope this would help motivate you a little bit too 🙂
My experience writing a Python tool that scraps number of citations of papers.
I started to pick up the basics of Python in the past few weeks – thanks to a 7-day hotel quarantine and a misaligned jetlag. I have been following Al Sweigart’s free to read book (and £13.99 course on Udemy) – Automate the Boring Stuff with Python. Last week, I’m proud to have written myself a little tool using Python!
Have you ever had a list of papers titles and thought “Hmm.. Wouldn’t be nice if they are sorted by number of citations?” This little gadget is the tool for you! (Yeah I am selling it too much >v<!) “Number of Citation” information is not readily available on Databases (apart from Scopus Web of Science). Fortunately, this information, whilst less reliable, is available on Google Scholar. The tool doesn’t do anything ground-breaking – you feed the program a list of paper titles, it scraps and print the number of citations of those papers on your spreadsheet.
There are existing solutions on the market that achieves this already, such as the Publish or Perish citation tool. I just thought this could be an entry-level task to test myself. “Written” is truly an overstatement – it’s more like copying and adapting codes from GitHub and Stack Overflow. But the sense of accomplishment is real.
One barrier I encountered was that, whilst the codes appear to work quite well independently when I was testing them, they do not seem to be performing consistently. One hour it worked, the next hour it stopped working. The codes were identical, I couldn’t understand how it wasn’t working. I was in hotel quarantine when this problem first appeared, and I was joking to my brother that I must have been blocked by Google – which I later realised was exactly the case!
Turns out, scrapping information from other people’s website may violate their terms and conditions – and could be borderline illegal. Sites like Amazon and Google (and many many others) set up timeouts that automatically blocks IP addresses when they detected a large number of requests (accesses/searches) within a short amount of time. I did not put in a time-out in my original codes, which sends in thousands of searches in minutes. No wonder I was blocked out!
Anyhow, this experience of testing and problem-solving has been fun! I began to understand more about the magic that fuels enthusiasm within the programming/software engineering community. I’m eager to be in a position to contribute to the conversation soon – one day I shall!
Sharing my experience using the Eisenhower’s Matrix & reflection on “time”.
Former US President Dwight Eisenhower was said to have popularised this time management system. By classifying tasks by it’s importance and urgency, Eisenhower’s Matrix was described as the holy grail to minimise distractions. I am sharing 2 problems I have with the matrix, how it does not fit my workstyle, and some wider reflections on living a highly-structured work/life-style.
Problem 1 – Everything is Important!!
I’d hate to think it is only me, but a great fallibility of mine when I started to use the matrix was that everything I thought of seems to be very important! At the beginning of my new role, there’s quite a bit of admin required setting up certain accounts, getting data access, or signing up to the relevant mailing lists etc. It meant that multiple conversations within and across institutes/ departments happened at the same time. It doesn’t make a lot of sense to rank or compare these tasks as they don’t appear to be too important, but I couldn’t do my job if these aren’t completed. On the other hand, I have got a list full of publications I am eager to catch up on the topic. I conflated “important to my job” and “important to satisfy my research interests”, and have been judging the importance of tasks with a fluctuating standard. This soon corrupted my matrix, with some tasks that are popping on and off every 2 days, and some staying on the matrix for eternity! Consequently, the bottom right quadrant – Not Urgent and Not Important – was always empty. I failed to utilise the tool to it’s fullest.
Problem 2 – Poorly Defined “Tasks”
The matrix is meant to be a task-focused tool, and not a progress-tracking tool to help facilitate learning. Continuation on the “never empty” tasks, apart from the misjudged importance, it is also the nature of the tasks that made them so difficult to tick off. An example is : learn Python. It is a key component of my work, highly important, probably quite urgent too [depends on what timescale we’re talking] if I want to have any real progress. But I could never cross off that task and call it done: even after I have completed 20 hours of tutorial videos, worked through a textbook, and coded my first little gadget on Python, I don’t feel confident enough to say that I have “learned Python”. The matrix is not meant for progress tracing, but rather for shortening to-do-lists. Some could argue that it was rather my non-SMART goals that the problem should be attributed to, and I shouldn’t judge the capabilities of the matrix based off that [SMART = Specific, Measurable, Achievable, Relevant, Time-bound]. However, I do think it is not realistic to map out the whole learning process into tiny bits of surrogate markers of achievement. Does the ability to copy-and-paste multiple sections of codes from GitHub mean I am capable of doing a task? How many errors or test and failures are tolerable to develop a new python gadget for a “good” coder? Was it the “coder’s mindset” I should be valuing, or should I be taking examinations to benchmark my progress? The checklist approach to learning did not work for me.
We find comfort in structure. We needed the structure guide our attention, to renounce our mastery over time. Time is being broken down to smaller units with higher precision to monitor progress, efficacy and production. We sure are living in a faced-paced world, but it is not just the pace, but the accuracy and rigidity of time has consequentially projected itself as the more appropriate way of living, as the “truth” that is more true than how time is experienced in the past. The passing of time is universal (well, sorry theoretical physicists), but the construct, measurement and experience of time is manufactured and constantly updated by the society, by us. We fabricated this need for speed that in turn necessitates the need for more precise measurement. In cultures where the obsession on time has (yet to) taken over, e.g., in African Culture, their way of living and experiencing time was often remarked pejoratively. Injustice might masks themselves as progress; Greed as philanthropic; Derogation as inclusion.
Despite our emphasis on time, and the structure that comes with it to help us master time, not having spent enough “Quality time” with loved ones was said to be one of the most common regrets on the death bed in modern times. Not all “times” are born equal. Our ability to just relax and enjoy the moment are being chipped away, checkbox by checkbox. The guilt of wasted time spill over and burdens us even more. I am sure the structure has helped a lot in the industrial revolution to get the factories rolling, perhaps it will serve a similar role as AI replace half of the labour force. How do we find quality in our time, befriend time and not to compete with time? Tools like the Eisenhower’s Matrix should help us build this healthy relationship with time, not to see ourselves as the Lords of time. Be humble!
[Finished reading Beyond Measure by James Vincent, whom described the history of measurement of time quite nicely.]
Last Saturday, my partner and I participated in the 5km Parkrun nearby. We’re all dandy, in other words, untrained. This is the first time we both are able to free ourselves from the shackles of the comfort from our beds on a Saturday morning.
The goal was to finish the run in one piece. We started off on a nice pace, dangling at around 400th place out of 600+ runners. Unlike the last time I joined, there is no muddy piles from rain. Little bits of tailwind accompanied the sunlight to give us an extra boost.
This extra boost came back to haunt us in unexpected ways. We were too used to running on treadmills, and we could not adapt to the natural landscapes. The tailwind must have also pushed us beyond our typical pace. My partner went slightly over her limit as her knees started to complain as we crossed the half-way point. We had to slow down.
As we squirm forward at the speed of rush-hour traffic in London, I started to feel the urge to just dash off and catch up with my pace. I reckon we must be at the tail of the crowd! My inner competitiveness wants to take over, it’s such a nice opportunity to set a personal best! My partner adds fuel to the fire and encourages me to go, “just wait for me at the finish line!”. Indeed, why shouldn’t I think less and run?
As I fall into the conundrum, I see how the situation somehow resembles my PhD journey. What is it that I value in this process? Was it to finish it as fast as I could in record breaking time? Or was it to take my time in learning, doing slow but meaningful science? It’s never either or, but setting a goal and stick with it would help me prioritise what’s truly important to me. At this point, it is to cherish my status as a student, to dive into theoretical puzzles, challenge myself with new skills, connect with people I dare not speaking to, and spend time with the ones I love.
Week 2 is a philosophical one. More reflection on how this world operates.
Week 2 is much less eventful comparing to week 1. It is likely a more truthful depiction of a typical week in the coming 3 years.
Measure and Routine Practices
Why we do what we do the way we do it?
Several constellations lined up to trigger this train of thought. I recently finished listening to Desperate Remedies: Psychiatry’s Turbulent Quest to Cure Mental Illness by British Sociologist Andrew Scull, whilst starting James Vincent’s first book, Beyond Measure: The Hidden History of Measurement. A challenge faced by psychiatrists in the 1970s as they put together DSM III was not a new one. It is a problem of establishing a reliable measure. As the French tried to establish the metre, the Chinese Emperors defining the tunes, and the Egyptians keeping time – to be reliable in what they measure. A proper measurement often relied on a naturally occurring (hence valid) phenomenon to establish it’s reliability, which is relatively easy to do for some of the things, etc. how sundials and waterclocks were used to track time. Mother nature became their guarantor. For other constructs, like friendship, happiness, rights and responsibility, we are less capable to do so, or at least haven’t found a way to reliably doing so yet. How we measure things tell us a lot about our understanding (or the lack) of the phenomenon.
The same applies to the research in health equity. What is being recorded and how they were recorded matters. And these directly influence what is available in our routine administrative data. For example, indicating the poor uptake of psychological therapy in an ethnically diverse catchment area do not simply mean that there is a strong stigma, but perhaps more entrenched distrust in the system, lack of support for people to access services etc. Moreover, alternative support provided by community members, cultural practices and are merely not recorded, and discounted from routine records. From this snap shot understanding of the “evidence” for poor therapy uptake, what could be a proper policy in response? It is impossible to tell just by data, and this is because of how we decided to frame and measure access.
It begs the question, who decides what to measure and how? Under this veil of evidence-based policy making, which people groups are routinely under-represented? I reflected on some of these question in my blog earlier this week (Reflecting on Ethnicity in Research – Challenging the Default). These are the questions I will keep in mind and keep interrogating myself as I carry on with my PhD research.
Starting to experience once again the joy and frustration of learning a new program. Successfully installed relevant packages – celebrates! Failed to reliably call my virtual environment – felt defeated… I have been forking people’s repos on Github but struggling to understand the process… Would appreciate any tips on picking up Python!