DECISION-MAKING CAPACITY ASSESSMENT EDUCATION FOR PHYSICIANS: CURRENT STATE AND FUTURE DIRECTIONS
Bibliographic record
Abstract
Objective: To examine the training needs of family physicians (FPs) regarding Decision-Making Capacity Assessments (DMCAs) and ways in which training materials, based on a DMCA Model, might be adapted for use by FPs. Setting: FPs practicing in a variety of settings: Primary Care, Day Programs, Home Living, Supportive/Assisted Living, Long-term Care, Restorative Care, Geriatric Clinic, and Geriatric inpatient/rehabilitation units in the Edmonton Zone, Alberta. Participants: FPs 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 (DMC), 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, four main themes emerged: increasing saliency of DMCAs due to an aging population, sub-optimal 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 on inter-professional (IP) teams are motivated to engage in DMCAs and utilize the DMCA Model for those assessments, several factors impede them from conducting DMCAs. The most notable factors are a lack of education, isolation from IP teams, uneasiness around managing conflict with families, fear of liability, and concerns regarding remuneration. Conclusion: This research project has helped to inform ways to better train and support FPs conducting DMCAs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".