Piloting the global capacity education e-tool: can capacity be taught to health care professionals across different international jurisdictions?
Bibliographic record
Abstract
Determining decision-making capacity is part of everyday business for health care professionals working with older adults. We used a modified Delphi approach to develop an inclusive curriculum for a capacity education e-tool with global application and clinical relevance to a range of disciplines. The tool comprised: (i) 25 questions forming a "pre-test" for the adaptive and personalized e-Learning platform; (ii) a learning module based on the participant's response to the "pre-test"; (iii) a "post-test" (the same baseline 25 questions) to test knowledge translation. The tool was tested on 31 health care professionals across Israel (8), Canada (15), and Australia (8) from the following disciplines: General Practitioners (GP) (19), Internal Medicine (1), Palliative Care GP (2); Palliative Care Physician (2), Geriatrician (2); and one of each: Psychologist, Occupational Therapist, Psychiatrist, Aged Care Researcher, and Aged Care Pharmacist. The mean baseline pre-test score was 19.1/25 (S.D. =1.61; range 15-22) and post-test score 21.7/25 (S.D.= 1.42; range 18-24); with a highly significant improvement in test scores (paired t-test P < 0.0001; t=10.81 on 30 df). This is the first such pilot study to demonstrate that generic capacity principles can be taught to health care professionals from different disciplines regardless of jurisdiction.
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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 | 0.038 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".