Why do some physicians choose to tackle inequities in healthcare?
Bibliographic record
Abstract
Abstract Background Despite the reputation of Canada’s healthcare system as being accessible to all Canadians, certain populations continue to face inequities within our healthcare system. In addition to promoting fairness, addressing healthcare inequities has the potential to reduce healthcare costs, which is increasingly important as healthcare costs continue to rise. Intentionally or otherwise, physicians are often leaders in healthcare teams, but there is a paucity of literature on physicians’ perceptions of the problem of healthcare inequities and their potential role in addressing inequities. In this pilot study, we use a grounded theory approach to explore contextual factors and mechanisms that associate with an individual physician’s involvement (or otherwise) in initiatives to reduce healthcare inequity. Methods Using purposeful sampling and a set of a priori questions, we interviewed ten physicians – five of whom self-identified as being actively involved and five not actively involved in addressing healthcare inequities – to explore potential reasons for physicians choosing to address the causes of healthcare inequities. Results We identified contextual barriers (e.g., lack of knowledge and time) and facilitators (prior experience, protected time, mentorship and system supports) that we interpreted as interacting with the underlying mechanism (motivation to address inequities) to influence a physician’s decision on whether or not to address healthcare inequities. Conclusion Based upon our findings we propose further studies to understand and/or overcome barriers to physicians being involved in addressing healthcare inequities.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".