Medical residents reflect on their prejudices toward poverty: a photovoice training project
Bibliographic record
Abstract
BACKGROUND: Clinicians face challenges in delivering care to socioeconomically disadvantaged patients. While both the public and academic sectors recognize the importance of addressing social inequities in healthcare, there is room for improvement in the training of family physicians, who report being ill-equipped to provide care that is responsive to the living conditions of these patients. This study explored: (i) residents' perceptions and experience in relation to providing care for socioeconomically disadvantaged patients, and (ii) how participating in a photovoice study helped them uncover and examine some of their prejudices and assumptions about poverty. METHODS: We conducted a participatory photovoice study. Participants were four family medicine residents, two medical supervisors, and two researchers. Residents attended six photovoice meetings at which they discussed photos they had taken. In collaboration with the researchers, the participants defined the research questions, took photos, and participated in data analysis and results dissemination. Meetings were recorded and transcribed for analysis, which consisted of coding, peer debriefing, thematic analysis, and interpretation. RESULTS: The medical residents uncovered and examined their own prejudices and misconceptions about poverty. They reported feeling unprepared to provide care to socioeconomically disadvantaged patients. Supported by medical supervisors and researchers, the residents underwent a three-phase reflexive process of: (1) engaging reflexively, (2) break(ing) through, and (3) taking action. The results indicated that medical residents subsequently felt encouraged to adopt a care approach that helped them overcome the social distance between themselves and their socioeconomically disadvantaged patients. CONCLUSIONS: This study highlights the importance of providing medical training on issues related to poverty and increasing awareness about social inequalities in medical education to counteract prejudices toward socioeconomically disadvantaged patients. Future studies should examine which elective courses and training could provide suitable tools to clinicians to improve their competence in delivering care to socioeconomically disadvantaged patients.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".