‘One size does not fit all’: Perspectives on diversity in community aged care
Bibliographic record
Abstract
OBJECTIVES: Typically, older people are viewed via a single health condition, yet health outcomes are likely impacted by the intersection of many individual characteristics. Promoting inclusive health care is underpinned by reducing bias, respectful communication and supporting individual needs and preferences. This study explores perspectives of community aged care workers on diversity training and implementing training into practice. METHODS: Ninety community aged care workers were telephone-interviewed three months after a one-day diversity training workshop. Interviews were audio-recorded, transcribed verbatim and analysed thematically. RESULTS: Five themes emerged: 'raising awareness'; 'reserving judgement'; 'confidence and empowerment to embed diversity into practice'; 'communicating effectively'; and 'thinking about change … but'. CONCLUSIONS: Diversity concepts were positively received, but applying diversity principles into practice is more difficult. Recommendations to promote inclusive health care included raising awareness of bias, communicating with inclusive language and embedding diversity concepts into community aged care practice by addressing individual, organisational and wider system constraints.
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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 | 0.034 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.038 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.010 |
| 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".