Navigating work-integrated learning and wellbeing in a mental health program for Australian First Nations students
Bibliographic record
Abstract
Work-integrated learning (WIL) is an important component of the Bachelor of Health Science (Mental Health), at Charles Sturt University, Australia. All students in this degree are of Aboriginal or Torres Strait Islander heritage and many are employed by area health services across two different states in traineeship positions that can then be used as WIL experiences in the program. A range of policies have been implemented across Australia and these policies note the importance of culturally safe workplaces for all people engaging in services that promote cultural responsiveness, integration, worker safety and care, collaboration, and innovation This paper will discuss the complex environments in which these Australian First Nations students engage in WIL. Wellbeing models for Indigenous peoples in Australia and New Zealand and how these can support students during WIL are explored. Implications and recommendations for the use of a wellbeing model to strengthen and support Indigenous students will be provided.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".