Developing a Decision-Making Capacity Assessment Clinical Pathway for Use in Primary Care: a Qualitative Exploratory Case Study
Bibliographic record
Abstract
BACKGROUND: With an ageing population, the incidence of dementia will increase, as will the number of persons requiring decision-making capacity assessments. For over 10 years, we have trained family physicians in conducting decision-making capacity assessments. Physician feedback post-training, however, has highlighted the need to integrate the decision-making capacity assessment process into the primary care context. The purpose of this study was to develop a decision-making capacity assessment clinical pathway for implementation in primary care. METHODS: A qualitative exploratory case-study design was used to obtain participants' perspectives regarding the utility of a visual algorithm detailing a decision-making capacity assessment clinical pathway for use in primary care. Three focus groups were conducted with family physicians (n=4) and allied health professionals (n=6) in two primary care clinics in Alberta. A revised algorithm was developed based on their feedback. RESULTS: In the focus groups, participants identified inconsistencies and a lack of standardization regarding decision-making capacity assessments within primary care, and provided feedback regarding a decision-making capacity assessment clinical pathway to make it more applicable to primary care. Participants described this pathway as appealing and straightforward; they also made suggestions to make it more primary care-centric. Participants indicated that the presented pathway would improve teamwork and standardization of decision-making capacity assessments within primary care. CONCLUSIONS: Use of a decision-making capacity assessment clinical pathway has the potential to standardize decision-making capacity assessment processes in primary care, and support least intrusive and least restrictive patient outcomes for community-dwelling older adults.
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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 | 0.041 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".