Five ways ‘health scholars’ are complicit in upholding health inequities, and how to stop
Bibliographic record
Abstract
Health scholars have been enthusiastic in critique of health inequities, but comparatively silent on the ways in which our own institutions, and our actions within them, recreate and retrench systems of oppression. The behaviour of health scholars within academic institutions have far reaching influences on the health-related workforce, the nature of evidence, and the policy solutions within our collective imaginations. Progress on health equity requires moving beyond platitudes like 'equity, diversity and inclusion' statements and trainings towards actually being and doing differently within our day-to-day practices. Applying complex systems change theory to identify, examine and shift mental models, or habits of thought (and action), that are keeping us stuck in our efforts to advance health equity is a promising approach. This paper introduces five common mental models that are preventing meaningful equity-oriented systems transformation within academia and offers ideas for shifting them towards progressively more productive, and authentic, actions by health scholars to advance health equity across systems.
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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 | 0.119 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.029 | 0.264 |
| Scholarly communication | 0.057 | 0.062 |
| Open science | 0.006 | 0.031 |
| Research integrity | 0.024 | 0.039 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".