IMPLEMENTING A NEW MODEL IN PRIMARY CARE FOR OLDER CANADIANS LIVING WITH FRAILTY
Bibliographic record
Abstract
Abstract Primary care may be the best place within the health system to coordinate care for older persons, but at present, it is poorly equipped to do so. Effective models for complex patients require appropriate targeting, patient/caregiver engagement, and care coordination. A large national project aims to co-design and implement a model in primary care that includes risk-stratification, patient engagement and care coordination techniques for older adults. This presentation focuses on the process of implementation in primary care. Grounded in the Consolidated Framework for Implementation Research, researchers worked with nine primary care sites in three Canadian provinces. Project implementation was completed in two phases. Pre-implementation: Interviews with providers (n=25) and older adults (n=8) were conducted to understand current practices and plan for implementation. Implementation: Researchers worked with sites to train staff and support implementation. Monitoring of the implementation process included Interviews with providers (n=20) and field notes. Data were analyzed using directed coding, following the framework. A number of learnings emerged: buy-in was required from the entire team, teams provided meaningful information to guide implementation, contributing to a sense of ownership, and it was important that intervention components were tailored to the needs at each site. Ongoing and frequent discussions with the team was necessary. Scheduling meetings and training sessions for providers was challenging due to the length of time away from direct patient care. A new primary care model for older adults living with frailty was implemented. Lessons from this project will be used to guide future implementation and spread.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".