Beyond witnesses: Moving health workers towards analysis and action on social determinants of health
Bibliographic record
Abstract
Abstract Although critical knowledge of social determinants of health empowers health professionals to confront the causes of inequitable health outcomes, healthcare professionals continue to feel powerless when faced with upstream social and structural issues. Using a case of the social medicine course conducted in Northern Uganda, and the 2016/2017 Uganda medical interns’ movement, we examine the significance of social medicine education in enhancing healthcare professionals’ skills set to address a structural force—medical internship policy. Data sources included key informants, policy documents, blogs, Facebook posts, and YouTube Videos. Data were analyzed using content analysis techniques. Healthcare workers drawing on critical skills and knowledge from the social medicine course training could perform self‐ and problem‐analysis centered within power dynamics; identify avenues to communicate issues of concern; implement constructive dialog and collaborate with stakeholders to influence and halt a medical internship policy discourse through protest on streets and legal channels. Social medicine training and principles empower health workers to function as actors with the required skills and knowledge to initiate and sustain tactical, effective, and meaningful health advocacy directed towards altering social determinants of health that perpetuate social disadvantage with subsequent impact on population health outcomes.
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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 | 0.057 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.024 | 0.035 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".