Who Said What Now and How? Evaluating Saskatchewan's Patient-Reported Measures in Primary Care
Bibliographic record
Abstract
Background: A team of patient partners, researchers, and health system collaborators evaluated the pilot implementation of patient reported metrics in primary care health networks in Saskatchewan, Canada with the intent to recommend best practices for provincial scale-up. Approach: The Saskatchewan Health Authority (SHA) is responsible for health services in Saskatchewan, Canada, including the delivery of primary care services across 38 health networks. Health networks are intended to connect teams of healthcare professionals and community partners to meet the needs of the people they serve. To ensure that the health system delivers care that matters to patients, the People Centred Measurement (PCM) working group, an SHA, patient, and health system partner collaboration, was established in 2020. In November 2022, the PCM working group launched an initiative called Integrating Patient Reported Data into Health Networks in Saskatchewan For this pilot project, an online survey was developed and implemented in 4 of 38 health networks to gather patient reports of their primary care experiences. University of Saskatchewan (USask) researchers and three patient partners who were embedded in the PCM working group engaged in a developmental evaluation of the pilot initiative to recommend policy options to scale up the implementation of patient reported data across Saskatchewan health networks. Working alongside the principal knowledge user who was the PCM working group director and a collaborator who led the development and implementation of the survey, one USask researcher attended all health network meetings and offered evaluative feedback in real time. Early in the evaluation, the researchers and patient partners produced an initial report suggesting the need to increase patient partner engagement and the limitations of a survey approach to the collection of patient reported experiences. Given the PCM imperative to implement patient reported measurement using the survey, the research team was encouraged to engage in a new data collection strategy. The research team pivoted to directly gather perspectives of the pilot project participants using semi-structured virtual interviews. Results: Based on 5 interviews with participants, patient partners and researchers presented the following recommendations at an end of project policy forum: a) Ensure resources for onboarding and support of patients and community members to contribute to ongoing People-Centred Measurement, b) Build processes that engage Indigenous communities, newcomers, hard-to-reach and under-served populations in a meaningful way that directly impacts their experience of care c) Foster relationships and collaboration across SHA portfolios to leverage expertise in People-Centred Measurement design and implementation d) Recognize the difference in capacity between remote, rural, and urban healthcare centres and co-design People-Centred Measurement strategies accordingly, and e) Continuously evaluate People-Centred Measurement implementation and adapt to changing social, economic, and environmental contexts. Implications: With the growing need to incorporate PCM into healthcare systems to deliver on the promise of patient-centred care, Saskatchewan is gradually improving its collection, analysis, and dissemination. Driven by patient partner engagement, findings from our evaluation will inform what is required for the successful collection of patient-reported experience and outcome measures to inform policies that will improve the health of SK people.
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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 | 0.038 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".