Diversity training for the community aged care workers: A conceptual framework for evaluation
Bibliographic record
Abstract
Older Australians are an increasingly diverse population, with variable characteristics such as culture, sexual orientation, socioeconomic status, and physical capabilities potentially influencing their participation in healthcare. In response, community aged care workers may need to increase skills and uptake of knowledge into practice regarding diversity through appropriate training interventions. Diversity training (DT) programs have traditionally existed in the realm of business, with little research attention devoted to scientifically evaluating the outcomes of training directed at community aged care workers. A DT workshop has been developed for community aged care workers, and this paper focuses on the construction of a formative evaluative framework for the workshop. Key evaluation concepts and measures relating to DT have been identified in the literature and integrated into the framework, focusing on five categories: Training needs analysis; Reactions; Learning outcomes, Behavioural outcomes and Results The use of a mixed methods approach in the framework provides an additional strength, by evaluating long-term behavioural change and improvements in service delivery. As little is known about the effectiveness of DT programs for community aged care workers, the proposed framework will provide an empirical and consistent method of evaluation, to assess their impact on enhancing older people's experience of healthcare.
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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 | 0.134 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.007 | 0.006 |
| 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".