Physician education on decision-making capacity assessment: Current state and future directions.
Bibliographic record
Abstract
OBJECTIVE: To examine FPs' training needs for conducting decision-making capacity assessments (DMCAs) and to determine how training materials, based on a DMCA model, can be adapted for use by FPs. DESIGN: A scoping review of the literature and qualitative research methodology (focus groups and structured interviews). SETTING: Edmonton, Alta. PARTICIPANTS: Nine FPs, who practised in various settings, who chose to attend a focus group on DMCAs. METHODS: A scoping review of the literature to examine the current status of physician education regarding assessment of decision-making capacity, and a focus group and interviews with FPs to ascertain the educational needs of FPs in this area. MAIN FINDINGS: Based on the scoping review of the literature, 4 main themes emerged: increasing saliency of DMCAs owing to an aging population, suboptimal DMCA training for physicians, inconsistent approaches to DMCA, and tension between autonomy and protection. The findings of the focus groups and interviews indicate that, while FPs working as independent practitioners or with interprofessional teams are motivated to engage in DMCAs and use the DMCA model for those assessments, several factors impede their conducting DMCAs. The most notable barriers were a lack of education, isolation from interprofessional teams, uneasiness around managing conflict with families, fear of liability, and concerns regarding remuneration. CONCLUSION: This pilot study has helped to inform ways to better train and support FPs in conducting DMCAs. Family physicians are well positioned, with proper training, to effectively conduct DMCAs. To engage FPs in the process, however, the barriers should be addressed.
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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 | 0.025 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".