The Future of the Regional Training Centres: Planning for Sustainability
Bibliographic record
Abstract
The main objective of the Regional Training Centres (RTCs) is to produce well qualified personnel within the fields of health services and nursing research.Through their collaborative efforts, each of the RTCs has created opportunities for conceptual and methodological competency, knowledge synthesis and knowledge translation and exchange for graduate students, as well as for community-based decision-makers across a variety of areas in applied health and nursing services research.Now, the RTCs face the challenge of envisioning their future.The task is not merely to describe what is, nor what will be, but rather to envision what could be.The purpose of this paper is to describe a plan for sustainability, not only financially but also with respect to management of human resources, student development and collaboration among the partners who make up the collective that is a Regional Training Centre. RésuméLe principal objectif des Centres régionaux de formation (CRF) était de produire du personnel qualifié dans le domaine de la recherche en services de santé et de soins infirmiers.Grâce à leurs efforts de collaboration, chaque CRF a créé des occasions d' application des compétences conceptuelles et méthodologiques, de synthèse des connaissances, et d' application et d' échange des connaissances pour les étudiants des cycles supérieurs, ainsi que d' établissement de partenariats communautaires avec des décideurs provenant d'une multitude de domaines de la recherche appliquée en services de santé et de soins infirmiers.Les CRF doivent maintenant envisager leur avenir.Il ne s' agit pas simplement de décrire ce qui est, ou ce qui sera, mais plutôt d' envisager ce qui pourrait être.Comme dans tous les exercices de visualisation, cet article vise à décrire un plan de durabilité, non seulement sur le plan financier, mais également en ce qui concerne la gestion des ressources humaines, le perfectionnement William Montelpare et al.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".