Practitioner perceptions of the health of Australian First Nations' Peoples: Preliminary findings
Bibliographic record
Abstract
OBJECTIVE: To probe health practitioner perceptions of Australian First Nations' Peoples' health and to compare these with reported burden of disease, social determinants and culturally safe health systems data. DESIGN: An online survey tool was used to collect self-reported responses from health practitioners to answer literature-derived questions regarding underlying issues in Australian First Nations' Peoples' health. SETTING: Responses to the survey were sought from rural and urban health service locations across all Australian states and territories. PARTICIPANTS: Allied health, nursing, pharmacy, psychology, social work and related discipline students and practitioners currently working or studying in Australia. MAIN OUTCOME MEASURE(S): Degrees of correspondence between health discipline student and practitioner perceptions on 'major health issues' and 'health systems issues' and published population health and health systems data. Metrics for 'connectedness to' and 'preparedness to engage with' Australian First Nations' Peoples were also reported. RESULTS: Significant differences between practitioner perceptions of 'major health issues' and the disease burden/social determinants published evidence, and with the 'culturally safe health systems' published evidence, were noted. Positive impacts of social and professional relationships (connectedness) between practitioners and First Nations' Peoples were demonstrated. CONCLUSIONS: The inclusion of basic population health and culturally safe health systems training in curricula for all genres of health practitioners appears to be indicated by these findings. Further, a meaningful role for the intentional nurture of social and professional relationships with Australian First Nations' Peoples across all health disciplines is suggested as part of efforts to address health systems and equity issues.
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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 | 0.012 | 0.028 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".