General practitioners’ perspective on poverty: a qualitative study in Montreal, Canada
Bibliographic record
Abstract
Background: Social inequalities in healthcare systems persist worldwide. Physicians' prejudices and negative attitudes towards people living in poverty are one of the determinants of healthcare inequalities. We know very little about general practitioners' (GPs) perceptions of poverty, which shape their attitudes. Objective: To identify the perceptions of poverty of GPs who deal with it in everyday practice. Methods: A qualitative study based on interviews with GPs working in deprived urban neighbourhoods. In-depth semi-structured interviews were conducted with physicians working in disadvantaged neighbourhoods in Montreal, Canada. Interviews were audio-recorded and transcribed verbatim. Analysis consisted of interview debriefing, transcript coding, and thematic analysis using an inductive and iterative approach. Results: Our study revealed two contrasting perceptions of poverty. The global conception of poverty referred to social determinants and was shared by the majority of physicians interviewed, while the moral conception, centring on individual responsibility, was shared by a minority of participants. Conclusion: The moral judgments and misunderstandings evidenced by GPs regarding poverty suggest avenues for improving general medical training. Understanding social determinants of health should be an important component of this training, to improve access to care for people living in poverty.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.031 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".