A square peg in a round hole: reflecting on using a participatory health research approach during my PhD
Bibliographic record
Abstract
When reflecting on my years as a doctoral student, I recall several questions that often came to mind throughout my journey: what is participatory health research?Is such an approach to research truly feasible in the pursuit of a doctoral degree?Is it worth it, or have I inadvertently made things more challenging for myself?My response to these questions has evolved dramatically alongside my growth and development throughout my PhD.I was presented with an opportunity to explore an approach to participatory health research firsthand; a process which included many jumps, twists, turns, and slides, and at times, left me feeling like a square peg in a round hole.Throughout this process, navigating the breadth of challenges and opportunities presented along the way, I also learned the importance of one's narrative -in particular, the growth and development made possible when researchers and participatory partners share our stories and reflect together.This paper is part of my story, through my account of 'our story'.It embraces a narrative-style approach to critical reflection on the participatory process throughout my doctoral studies, emphasising the key challenges posed when working within the boundaries of traditional academic structures.I provide a reflexive account of how these challenges were navigated, which created a range of opportunities at both a theoretical and practical level.I conclude with a response to these initial questions and a hopeful call for change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.173 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.057 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.011 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".