Pilot of a Priorities Aligned Decision-Making Microskills Curriculum
Bibliographic record
Abstract
Abstract Patient Priorities Care (PPC) provides an approach for identifying patient health care priorities and aligning care with those priorities. Training in PPC has demonstrated high overall satisfaction and its implementation is associated with positive patient centered outcomes. Yet, the current training approach is time and resource intensive. This work describes the adaptation and piloting of a PPC curriculum to address these barriers. An interdisciplinary group of clinician educators collaborated to divide PPC content into 8 core skills and organize it into graphic power point and PDF documents. Educators are piloting materials with learners. A baseline survey was administered to pilot participants (educators and learners). Participants will complete a post-pilot survey to assess feasibility and ease of use of materials. Eighteen learners have completed the baseline survey. Most learners were from either Internal Medicine (22%) or Geriatric Medicine (61.1%) disciplines. More than half (55.6%) had some prior training in priorities-aligned care. On a scale of 1-5 (1 not confident and 5 extremely confident), learners reported being moderately confident (range 22.2%-44.4%), quite confident (range 27.8%-44.4%) or extremely confident (range 11.1%-22.2%) in the 8 core skills. A high level of baseline confidence in the core skills will allow assessment of content feasibility. Next steps include evaluating ease of use of materials and refining the materials and assessment methods based on feedback. We will then disseminate curricular materials and assessment methods to educators nationally. We hope to demonstrate that the adapted curriculum will ensure durable implementation of PPC across multiple health care settings.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".