Identification of Aboriginal and Torres Strait Islander status by general practice registrars: Confidence and associations.
Bibliographic record
Abstract
BACKGROUND: Identification of Aboriginal and Torres Strait Islander status in healthcare settings is essential for the delivery of culturally appropriate care. Under-identification is common and practitioner confidence is a known barrier. OBJECTIVE: The objective of this research was to document the self-reported confidence of general practice registrars in identifying the Aboriginal and Torres Strait Islander status of their patients, and associations of this confidence. METHODS: This research used cross-sectional analysis of survey and patient encounter data of general practice registrars training across five Australian states. RESULTS: Of the 698 registrars (97.5% response rate) who participated in the study, 74.5% had a high level of confidence in identifying a patient's Aboriginal and Torres Strait Islander status. Older and more senior registrars had significantly greater confidence. There was also a significant association with the registrars' training provider. DISCUSSION: More than a quarter of registrars reported low confidence for this basic consultation skill. Our findings will inform general prac-tice vocational training and continuing professional development, and reinforce the importance of a comprehensive, system-wide approach to the identification of patients' Aboriginal or Torres Strait Islander status.
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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 | 0.005 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".