Envisioning a socially accountable doctor: a three-axis curriculum emerging from final-year medical student reflections
Bibliographic record
Abstract
Social accountability in health professions education is important for the reduction of health disparities. There is a need for the development of curricula which begin to produce graduates who are responsive to community needs. These curricula need to include dialogues with communities, deep reflection and a transformative perspective. This study used a grounded theory approach to explore the perceptions of social accountability amongst final-year medical students. These students grappled with the definition of social accountability but described it as the tension between obligation and a willingness to serve. Five themes regarding social accountability were drawn from the students’ feedback: ‘it’s poorly defined’; ‘web of interconnected relationships’; ‘losing my heart and losing my compassion’; ‘more wide-angled view of things’ and ‘if I don’t go there, then who will go?’. These themes are connected through relational statements of three curricular axes of reflective practice, complexity and meaningful relationships. In each of these axes, participants identified catalysts and detractors for the progressive development of an accountable medical graduate. How to cite this article: GREEN-THOMPSON, Lionel; MCINERNEY, Patricia; WOOLLARD; Robert. Envisioning a socially accountable doctor: a three-axis curriculum emerging from final-year medical student reflections Scholarship of Teaching and Learning in the South, v. 2, n. 1, p. 76-94, Apr. 2018. Available at: Available at: http://sotl-south-journal.net/?journal=sotls&page=article&op=view&path%5B%5D=27 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".