Capacity development in health systems and policy research: a survey of the Canadian context
Bibliographic record
Abstract
BACKGROUND: Over the past decade, substantial global investment has been made to support health systems and policy research (HSPR), with considerable resources allocated to training. In Canada, signs point to a larger and more highly skilled HSPR workforce, but little is known about whether growth in HSPR human resource capacity is aligned with investments in other research infrastructure, or what happens to HSPR graduates following training. METHODS: We collected data from the Canadian Institutes of Health Research, Canada's national health research funding agency, and the Canadian Association for Health Services and Policy Research on recent graduates in the HSPR workforce. We also surveyed 45 Canadian HSPR training programs to determine what information they collect on the career experiences of graduates. RESULTS: No university programs are currently engaged in systematic follow-up. Collaborative training programs funded by the national health research funding agency report performing short-term mandated tracking activities, but whether and how data are used is unclear. No programs collected information about whether graduates were using skills obtained in training, though information collected by the national funding agency suggests a minority (<30%) of doctoral-level trainees moving on to academic careers. CONCLUSIONS: Significant investments have been made to increase HSPR capacity in Canada and around the world but no systematic attempts to evaluate the impact of these investments have been made. As a research community, we have the expertise and responsibility to evaluate our health research human resources and should strive to build a stronger knowledge base to inform future investment in HSPR research capacity.
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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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.021 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".