Highlights of the Canadian Frailty Network (CFN) Highly Qualified Personnel Poster Session
Bibliographic record
Abstract
Background: Most adults prefer that their health-care providers initiate advance care planning (ACP) conversations.Yet, primary care providers (PCPs) rarely discuss ACP with patients.The goal of this study is to implement and evaluate communication training for PCPs to improve the quantity and quality of ACP conversations.Methods: This is a quality improvement project in a large interprofessional academic family medicine team.PCPs (n= 30) attended 2.5-hour Serious Illness Conversation Guide (SICG) training workshops, adapted from Ariadne Labs, to improve their ACP capabilities.Participants completed preand post-workshop self-assessments.PCPs will use the SICG to conduct ACP with 3 patients each.Eligible patients are aged 65 or older, having any diagnosis of chronic illness or frailty that shortens lifespan.PCPs and patients will complete questionnaires gauging their experiences.Results: Post-workshop assessments reported improvements in confidence, prognosis sharing, and exploring patient values and trade-offs.PCPs reported the workshops as highly effective in improving ACP skills.Results to be presented will also include PCP, patient, and family member ACP satisfaction.Conclusions: Results will inform adaptations for further primary care training and implementation strategies. The Effects of the Serious Illness Care Program (SICP) on Health Care Resource Utilization (HRU)
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.129 | 0.021 |
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".