University Collaboration in Delivering Applied Health and Nursing Services Research Training
Bibliographic record
Abstract
In 2001-2002, the Canadian Health Services Research Foundation (CHSRF) and the Canadian Institutes of Health Research (CIHR) committed 10 years of funding for the creation and implementation of three Regional Training Centres to build capacity in health services and policy research in the Atlantic, Ontario and Western regions of Canada and one training centre in Quebec to focus on the development of nursing services researchers.Each RTC comprises several universities that collaborate to deliver the graduate training.The authors of this paper describe the consortium-related features of the RTCs, including approval processes, formal agreements, governance, communication, students, curriculum, administration and use of educational technology.The discussion outlines the benefits and challenges of university collaboration for participating students, faculty and universities and summarizes lessons learned. RésuméEn 2001-2002, la Fondation canadienne de la recherche sur les services de santé (FCRSS) et les Instituts de recherche en santé du Canada (IRSC) ont alloué 10 ans de financement pour la création et la mise sur pied de quatre Centres régionaux de formation (CRF) -en Ontario, au Québec et dans les régions de l' Atlantique et de l'Ouest du Canada -afin d' accroître la capacité dans le domaine de la recherche en services et en politiques de santé et de favoriser la formation de chercheurs en services infirmiers.Chaque CRF comprend plusieurs universités qui collaborent pour offrir le programme d' études supérieures.Les auteurs de cet article décrivent les caractéristiques des CRF qui s' apparentent à celles des consortiums, y compris les processus d' approbation, les ententes officielles, la gouvernance, la communication, les étudiants, les programmes d' études, l' administration et l' emploi de la technologie d' enseignement.L'article présente les avantages et les défis de la collaboration universitaire pour les étudiants, le corps professoral et les universités et résume les leçons apprises.
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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 | 0.144 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".