Medical education for equity in health: a participatory action research involving persons living in poverty and healthcare professionals
Bibliographic record
Abstract
BACKGROUND: Improving the knowledge and competencies of healthcare professionals is crucial to better address the specific needs of persons living in poverty and avoid stigmatization. This study aimed to explore the needs and expectations of persons living in poverty and healthcare professionals in terms of medical training regarding poverty and its effects on health and healthcare. METHODS: We conducted a participatory action research study using photovoice, a method using photography, together with merging of knowledge and practice, an approach promoting dialogue between different sources of knowledge. Nineteen healthcare professionals and persons from an international community organization against poverty participated in the study. The first phase included 60 meetings and group sessions to identify the perceived barriers between persons living in poverty and healthcare teams. In the second phase, sub-committees deployed action plans in academic teaching units to overcome barriers identified in the first phase. Data were analysed through thematic analysis, using NVivo, in collaboration with five non-academic co-researchers. RESULTS: Four themes in regard to medical training were highlighted: improving medical students' and residents' knowledge on poverty and the living conditions of persons living in poverty; improving their understanding of the reality of those people; improving their relational skills pertaining to communication and interaction with persons living in poverty; improving their awareness and capacity for self-reflection. At the end of the second phase, actions were undertaken such as improving knowledge of the living conditions of persons living in poverty by posting social assistance rates, and tailoring interventions to patients' reality by including sociodemographic information in electronic medical records. Our findings also led to a participatory research project aiming to improve the skills and competency of residents and health professionals in regard to the quality of healthcare provided to persons living in poverty. CONCLUSIONS: Medical training and residency programs should aim to improve students' and residents' relational skills, more specifically their communication skills, as well as their awareness and capacity for self-reflection, by helping them to identify and recognize their biases, and limitations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".