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 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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".