A critique of measures of emotion and empathy in First Peoples’ cultural safety in nursing education: A systematic literature review
Bibliographic record
Abstract
BACKGROUND: In Australia, undertaking cultural safety education often evokes strong emotional responses by health students. Despite the potential for emotion to drive transformative learning in this space, measures of emotion are uncommon. AIM: To review existing tools that intend to measure emotional components of learning in relation to cultural safety education. METHODS: Articles published in English from January 2005 to January 2020; reported studies from Australia, New Zealand, Canada and United States of America; and measured an emotional construct/s after an education intervention offered to university students enrolled in a health programme were included. Studies were assessed for quality according to the Critical Appraisals Skills Programme criteria. RESULTS: Eight articles were reviewed; five conducted in the United States of America, and three in Australia. Intervention type, measures, methodological rigour and outcomes varied. Studies predominately measured empathy, guilt and/or fear. CONCLUSIONS: Although students' emotional responses were measured, processes for students to reflect upon these reactions were not incorporated in the classroom. The review has implications for future research and curricula through developments in measuring and acting upon emotion in cultural safety education for nursing students in Australia.
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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 | 0.208 | 0.442 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".