Designing a framework for primary health care research in Canada: a scoping literature review
Bibliographic record
Abstract
BACKGROUND: Despite significant investments to improve primary health care (PHC) delivery in Canada, provincial health care systems remain fragmented and uncoordinated. Canada's commitment to strengthening PHC should be driven by robust research and evaluation that reflects our health policy priorities and responds to the needs of the population. One challenge facing health services researchers is developing and sustaining meaningful research priorities and agendas in an overburdened, complex health care system with limited capacity for PHC research and support for clinician researchers. METHODS: A scoping review of the literature was conducted to examine PHC research priorities in Canada. We compared national research priorities for PHC to research priorities being considered in the province of Alberta. Our scoping review was guided by the following questions: (1) What are the research priorities for PHC in Canada?; and (2) What process is used to identity PHC research priorities? RESULTS: Six key theme areas for consideration in setting a PHC research agenda were identified: research in practice, research on practice, research about practice, methods of priority setting, infrastructure, and the intersection of PHC and population/public health. These thematic areas provide a new framework for guiding PHC research in Canada. It was developed to generate best practices and new knowledge (i.e., innovation), transform PHC clinical practice or support quality improvement (i.e., spread), and lead to large-scale health care system transformation (i.e., scale). CONCLUSIONS: Priority-driven research aims to answer questions of key importance that are likely to have a significant impact on knowledge or practice in the short to medium term. Setting PHC research priorities ensures funded research has the greatest potential population health benefit, that research funding and outputs are aligned with the needs of practitioners and decision makers, and that there is efficient and equitable use of limited resources with less duplication of research effort. Our findings also suggest that a common research priority framework for PHC research in Canada would ensure that research priority-setting exercises are grounded in an evidence-based process.
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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 | 0.270 | 0.285 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.080 | 0.092 |
| Science and technology studies | 0.024 | 0.024 |
| Scholarly communication | 0.035 | 0.016 |
| Open science | 0.014 | 0.019 |
| Research integrity | 0.012 | 0.010 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".