Optimizing participation in the OECD PaRIS Project: Lessons learned in Saskatchewan
Bibliographic record
Abstract
Context: Leading the OECD PaRIS Project in Saskatchewan (SK) was an integrated primary care collaborative team consisting of primary care providers (PCPs), people with lived experience (PWLE) aka patients, health system partners and researchers. Objective: To describe the recruitment strategies and key lessons from the engagement of PCPs and PWLE that participated in the PaRIS Project in Saskatchewan, Canada. Study Design and Analysis: A participatory approach to cross-sectional surveys was facilitated through the building and nurturing of relationships based on trust and transparency with all members of the research team. Descriptive and inferential statistics were undertaken. Setting: Primary care clinics across Saskatchewan. Population Studied: Eligibility criteria involved PCPs (family physicians and/or nurse practitioners) who facilitated a panel of patients; and PWLE who were 45 years of age or older, with or without a chronic condition, and who had documented appointments with an eligible PCP in the past six months. Instrument: Survey developed and approved by the OECD-PaRIS Working Group. Outcome Measures: Patient-Reported Experience Measures (PREMS) and Patient-Reported Outcome Measures (PROMS). Results: Fifty healthcare providers from 10 different practices resulted in 1,324 returned surveys (50 from PCPs; 1,274 from PWLE) within 4-months (July to October 2023). Recruitment success was built on integrating PCPs, as co-researchers, into the process. This relationship-driven approach was facilitated by: attending and participating in events attended by PCPs; invited luncheon meetings re: implementing PaRIS into practice; engaging clinic administrators; recognizing data ownership; creating a feedback loop; and, centering the perspectives of the PWLE in the recruitment/engagement processes. Recruitment opportunities for improvement were: survey length; language complexity; technical complexity; lack of time to build relationships with Indigenous peoples; and, the additional responsibility placed on providers to facilitate recruitment of PWLE. Conclusions: This study revealed recruitment strategies and insights gained from engaging PCPs and PWLE into the PaRIS Project in Saskatchewan. The results/findings underscore the effectiveness of participatory strategies in engaging/recruiting participants for PREMS and PROMS, highlighting the importance of early engagement, fostering trusting relationships, and acknowledging data ownership.
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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 | 0.044 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".