Building Canada's health research capacity within the framework of the Canadian Institutes of Health Research
Bibliographic record
Abstract
The establishment of the Canadian Institutes of Health Research (CIHR) generated considerable excitement about the capacity for health research in Canada. The long term success of the CIHR will be determined, in part, by its ability to recruit, train and retain a cadre of talented researchers. During a workshop to develop the research agenda for one of the proposed institutes within the CIHR, a national, multidisciplinary group of clinical and basic science research trainees were invited to present their views about the challenges that face Canadian researchers of tomorrow. The objective of this paper is to present the challenges associated with recruiting, training and retaining health researchers, and to identify new opportunities provided by the creation of the CIHR. The present paper concludes with suggestions that may improve the success of researchers and, ultimately, the success of the CIHR.
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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 | 0.085 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.022 | 0.017 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".