Following the Money: A Who, How Much and Where Picture of Primary Care Research Investment in Canada
Bibliographic record
Abstract
Context: Canada under-invests in research related to primary care. Most Canadians (86%) have a regular care provider and family physicians deliver 54% of all medical services. However, primary care research accounts for less than 3% of grant funding within Canada’s largest health research funding agency, the Canadian Institute for Health Research (CIHR). This disconnect calls for further study of primary care research investment. Objective: To inform development of future research support strategies, this study examines CIHR’s historical primary care research funding trend and faculty of medicine research revenue variations across disciplines, jurisdictions and funding sources. Study Design and Analysis: Descriptive analysis using secondary data. Datasets: CIHR grant funding data covers the time period 2000-01 to 2020-21. The Association of Faculties of Medicine of Canada (AFMC) provided detailed research revenues data for 2018. Population Studied: CIHR data includes discretionary primary care funding through investigator initiated, priority area and training and career support programs. AFMC data covers comprehensive research revenue sources for all Canadian faculties of medicine, covering basic, clinical and health science disciplines. Outcome Measures: Sum and percent distribution of research funding/revenues. Results: CIHR’s total funding for primary care research increased from $922,627 in fiscal 2000-01 to $24,179,100 in 2020-21 (inflation adjusted CDN dollars). In 2000-01, primary care accounted for 0.25% of CIHR’s total discretionary funding, and in 2020-21 it accounted for 2.39%. In 2018, Family Medicine accounted for 1.51% of all faculty of medicine research revenues. Clinical science disciplines, including Family Medicine, accounted for 49.04%, basic science accounted for 30.86%, and all other disciplines accounted for 20.10%. Provincial governments are the largest revenue source for Family Medicine research (36.82%), followed by the federal government (27.24%) and not-for-profit agencies (15.05%). Ontario accounts for a relatively high share of faculty of medicine Family Medicine research revenues. Conclusions: Investment in primary care research has increased significantly since 2000-01, but still accounts for relatively little of CIHR’s discretionary funding. Comparative analyses of research revenues across disciplines, faculties of medicine, jurisdictions, and funding sources sheds light on drivers of Family Medicine research in Canada.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.017 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".