Recruiting Patients with Complex Care Needs to a Case Management Program in Primary Care: Engaging Patient Partners
Bibliographic record
Abstract
Context: PriCARE is a patient-oriented research program to implement and assess case management (CM) for patients with complex care needs that traditional primary care services cannot address. This initiative spans five jurisdictions including anglophone and francophone representation, as well as a First Nation community, with PriCARE engaged public patient partners. Objective: To highlight how patient partners participated in each step of the work on this research team, and specifically with recruitment. Study Design: Participatory approach involving patient partners and academic researchers. Setting: Research program implemented in primary care clinics in Quebec, New Brunswick, Newfoundland-and-Labrador, Nova Scotia and Saskatchewan. Population Studied: Patients with complex care needs. Program: In each of five Canadian jurisdictions, one to two patient partners, that is, patients or family caregivers of patients that experienced complex care needs and the health care system, were engaged to work closely with the academic researchers. The patient partners participated in a community of practice meeting on a regular basis. The patient partners were involved in each step of the initiative and participated in the decision-making process. Results: Patient partners reviewed and contributed to the initial grant application, and subsequently to the development of tools for recruitment and data collection, testing of tools, sharing insights on data analysis and interpretation, developing simple, understandable infographics videos, abstracts, presentations and contributing to published manuscripts to communicate the value of CM to potential patient participants. Specifically patient partners played a central role in recruitment of patient participants to PriCARE. Conclusions: Patient partners keep the research team focused on the study participants, their needs and how the health care system must function to meet the needs of the patient and their families.
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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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".