Protest and trainees in the health professions: Exploring the global landscape of recent protest action
Bibliographic record
Abstract
The use of protest as a form of professional resistance by trainees in the health professions has gained public attention in recent years. However, scholarship on the drivers and dynamics of protest by health professions' trainees remains limited, undertheorized and largely focused on high-income countries. Our goal in this paper is to provide the first known global landscape of protests by trainees in the health professions and to explore what protest demands by these trainees reveal about their structural position, agency and power, their professional identities and the implications for future health policy. We used a sequential explanatory mixed method design. First, we quantitatively analysed protest event data from the Armed Conflict Location and Event Data (ACLED) project focusing on January 2021 to April 2024. Next, we analysed textual information on each protest event and inductively developed an index of protest demands. Finally, we selected five illustrative clusters of protest demands and employed qualitative case study methodology drawing on publicly available data to examine protest actors, dynamics and outcomes. Drawing on our analysis of these data, we highlight the crucial perspective of trainees, who, by virtue of their status as 'interstitial' workers, occupy a liminal status as both students and workers-as such, they are able to provide an important lens through which to view and critique the health system. Navigating cultures of silence, obsequence and sometimes oppression enabled by the hidden curriculum in health professions' education (particularly medicine), trainees utilise protest as a means to visibilize their discontent. However, their discontent does not fit clear binaries, with motivations across a spectrum of professional self-interest and the public good. Structural challenges in health professions' education as expressed by protestors, compensation, working and living conditions, violence, harassment and abuse, poor management and other challenges, suggest major lacunae in prioritization and resourcing of health professions' education, particularly in Global South contexts.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.000 |
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".