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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".