Health services and policy research in the first decadeat the Canadian Institutes of Health Research
Bibliographic record
Abstract
BACKGROUND: Health services and policy research is the innovation engine of a health care system. In 2000, the Canadian Institutes of Health Research (CIHR) was formed to foster the growth of all sciences that could improve health care. We evaluated trends in health services and policy research funding, in addition to determinants of funding success. METHODS: All applications submitted to CIHR strategic and open operating grant competitions between 2001 and 2011 were included in our analysis. Age, sex, size of research team, critical mass, season, year and research discipline were retrieved from application information. A cohort of 4725 applicants successfully funded between 2001 and 2005 were followed for 5 years to evaluate predictors of continuous funding. Multivariate generalized estimating equation logistic regression was used to estimate predictors of funding success and sustained funding. RESULTS: Between 2001 and 2011, 80 163 applications were submitted to open and strategic grant competitions. Over time, grant applications increased from 327 to 1137 per year, and annual funding increased from $12.6 to $48.0 million. Grant applications from young male researchers were more likely to be funded than those from female researchers (odds ratio [OR] 1.40, 95% confidence interval [CI] 1.01-1.95), as were applications from larger research teams and institutions with a large critical mass. Only 24.0% of scientists whose first funded grant was in health services and policy research had sustained 5-year funding, compared with 52.8% of biomedical scientists (OR 0.34, 95% CI 0.24-0.49). INTERPRETATION: The CIHR has successfully increased the amount of health services and policy research in Canada. To enhance conditions for success, researchers should be encouraged to work in teams, request longer duration grants, resubmit unsuccessful applications and affiliate themselves with institutions with a greater critical mass.
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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 | 0.045 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".