Professional training on shared decision making with older adults living with neurocognitive disorders: a mixed-methods implementation study
Bibliographic record
Abstract
BACKGROUND: Shared decision making with older adults living with neurocognitive disorders is challenging for primary healthcare professionals. We studied the implementation of a professional training program featuring an e-learning activity on shared decision making and five Decision Boxes on the care of people with neurocognitive disorders, and measured the program's effects. METHODS: In this mixed-methods study, we recruited healthcare professionals in family medicine clinics and homecare settings in the Quebec City area (Canada). The professionals signed up for training as a continuing professional development activity and answered an online survey before and after training to assess their knowledge, and intention to adopt shared decision making. We recorded healthcare professionals' access to each training component, and conducted telephone interviews with a purposeful sample of extreme cases: half had completed training and the other half had not. We performed bivariate analyses with the survey data and a thematic qualitative analysis of the interviews, as per the theory of planned behaviour. RESULTS: Of the 47 participating healthcare professionals, 31 (66%) completed at least one training component. Several factors restricted participation, including lack of time, training fragmentation into several components, poor adaptation of training to specific professions, and technical/logistical barriers. Ease of access, ease of use, the usefulness of training content and the availability of training credits fostered participation. Training allowed Healthcare professionals to improve their knowledge about risk communication (p = 0.02), and their awareness of the options (P = 0.011). Professionals' intention to adopt shared decision making was high before training (mean ± SD = 5.88 ± 0.99, scale from 1 to 7, with 7 high) and remained high thereafter (5.94 ± 0.9). CONCLUSIONS: The results of this study will allow modifying the training program to improve participation rates and, ultimately, uptake of meaningful shared decision making with patients living with neurocognitive disorders.
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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 | 0.034 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".