"Between Two Worlds": Healthcare Decision-maker Engagement with Regional Training Centres
Bibliographic record
Abstract
The engagement between Regional Training Centres (RTCs) and healthcare decision-makers within the context of Applied Health and Nursing Services Research (AHNSR) takes many forms, and is critical to the development of the next generation of researchers. Such engagement supports the concept of linkage and exchange by inculcating in students and healthcare decision-makers alike an understanding of and respect for each other's worlds. This process builds bridges of immense importance to contemporary healthcare. The authors of this paper discuss the rationale for such engagement and describe the varied types of interaction between students and faculty with healthcare decision-makers and organizations. Bridging these two worlds for mutual advantage represents an innovative and highly successful strategy for graduate education in AHNSR. While this effort is not without challenges, the work of each world is relevant and valuable to the other and to the Canadian public.
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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 | 0.044 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.031 |
| Scholarly communication | 0.032 | 0.017 |
| Open science | 0.005 | 0.035 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".