Decision‐making capacity assessment education
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: As the elderly population and associated number of persons with dementia increase, so does the need for decision-making capacity assessments (DMCAs). Many healthcare professionals (HCPs), however, do not feel prepared to conduct DMCAs. We have provided 4-h DMCA workshops to HCPs since 2006 and offered physicians either a 3-h or 2-day DMCA workshop from 2013 to present. We evaluated the effectiveness of the workshops on self-reported key concepts of capacity. DESIGN: Pre-/post-test design. SETTING: DMCA workshops for physicians across Canada and HCPs within Alberta. PARTICIPANTS: Two hundred and eighty-one physicians and 835 HCPs. MEASUREMENTS: Pre-/post-workshop ratings on level of comfort with and understanding of 15 core DMCA concepts using 4-point Likert-type items. RESULTS: For the DMCA workshops among physicians, ten 3-h workshops were held in 2014-2015 with 166 participants and seven 2-day workshops, between 2014 and 2018, with 115 participants. With respect to the self-report on core DMCA concepts, at least 62.7% (range: 62.7%-89.6%) of physician participants had higher post-workshop ratings (sign test; p < 0.001) than pre-workshop ratings for all core DMCA concepts. For the DMCA workshops among HCPs, 4-h workshops were delivered to 835 HCP participants from 2008 to 2012. At least 49.6% of participants (range: 49.6%-78.9%) had increased post-workshop ratings (sign test; p < 0.001) for all level of comfort items with and understanding of core DMCA concepts. CONCLUSION: There is a need for DMCA training for physicians and HCPs. The content and method of the workshops are effective at enhancing self-reported level of comfort with and understanding of core DMCA concepts.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".