Perceived preparedness and training needs of new graduate physiotherapists’ working with First Nations Australians
Bibliographic record
Abstract
INTRODUCTION: There is a considerable and ongoing health gap experienced by First Nations Australians. Physiotherapists play an integral role in the health care of this population; however, little is known about new graduate preparedness and training needs to work in a First Nations context. OBJECTIVE: To explore the perceptions of new graduate physiotherapists regarding their preparedness and training needs for working with First Nation Australians. METHODS: Qualitative telephone, semi-structured interviews of new graduate physiotherapists (n = 13) who have worked with First Nations Australians in the last two years. Inductive, reflexive thematic analysis was used. RESULTS: Five themes were generated: 1) limitations of pre-professional training; 2) benefits of work integrated learning; 3) 'on the job' development; 4) intrapersonal factors and efforts; and 5) insights into improving training. CONCLUSION: New graduate physiotherapists perceive that their preparedness to work in a First Nations health context is supported by practical and varied learning experiences. At the pre-professional level, new graduates benefit from work integrated learning and opportunities that evoke critical self-reflection. At the professional level, new graduates express a need for 'on the job' development, peer supervision, and tailored professional development, that focuses on the unique perspectives of the specific community in which they work.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".