Understanding practitioner professionalism in Aboriginal and Torres Strait Islander health: lessons from student and registrar placements at an urban Aboriginal and Torres Strait Islander primary healthcare service
Bibliographic record
Abstract
Aboriginal and Torres Strait Islander peoples continue to be pathologised in medical curriculum, leaving graduates feeling unequipped to effectively work cross-culturally. These factors create barriers to culturally safe health care for Aboriginal and Torres Strait Islander peoples. In this pilot pre-post study, the learning experiences of seven medical students and four medical registrars undertaking clinical placements at an urban Aboriginal and Torres Strait Islander primary healthcare service in 2014 were followed. Through analysis and comparison of pre- and post-placement responses to a paper-based case study of a fictitious Aboriginal patient, four learning principles for medical professionalism were identified: student exposure to nuanced, complex and positive representations of Aboriginal peoples; positive practitioner role modelling; interpersonal skills that build trust and minimise patient-practitioner relational power imbalances; and knowledge, understanding and skills for providing patient-centred, holistic care. Though not exhaustive, these principles can increase the capacity of practitioners to foster culturally safe and optimal health care for Aboriginal peoples. Furthermore, competence and effectiveness in Aboriginal health care is an essential component of medical professionalism.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".