Measuring attitude change in nursing students after completion of a First Nations peoples’ health unit: Embedding a validated tool in learning and assessment practices
Bibliographic record
Abstract
The health inequities experienced by Aboriginal and Torres Strait Islander peoples, rooted in the historical and ongoing negative impacts of colonisation and disrupted traditional lifestyles, sees higher rates of illness and hospitalisations, increased morbidity and higher premature death rates than other Australians. Nurses represent the majority of Australia’s health workforce and nursing students must have knowledge to provide culturally safe care to Australia’s Aboriginal and Torres Strait Islander peoples. An Aboriginal and Torres Strait Islander Peoples’ Health and Wellbeing unit was developed and introduced into an undergraduate nursing curriculum. This project aimed to determine the effectiveness of the unit content in changing the attitude of nursing students towards care of First Nations peoples. A quasi-experimental design was used and included all undergraduate nursing students commencing this unit in 2020 at one university in Western Australia. Students completed a validated questionnaire prior to engaging with unit learning materials and again at completion of the unit. Complete paired data was available for 339 participants. Results demonstrated statistically significant change in attitude following completion of the unit. Embedding targeted education concerning Aboriginal and Torres Strait Islander peoples’ health and wellbeing positively influences attitudes in caring for this population of patients.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".