Strategic Approach to Building Research Capacity in Inter-professional Education and Collaboration
Bibliographic record
Abstract
The purpose of this paper is to describe the process used to initiate research capacity building in a community of practice (CoP) focused on the research and evaluation of inter-professional education and collaboration. This CoP, composed of members from across Canada, is a committee of the Canadian Interprofessional Health Collaborative (CIHC), a national collaborative that aims to advance inter-professional education and collaboration in healthcare. The committee mapped recommendations that emerged from a number of CIHC reports onto a research capacity building framework. The expertise of the diverse members in conjunction with this unique mapping process allowed the committee to identify its long-term research and evaluation objectives and strategies. This resulted in the formation of three working groups, each tasked with activities that contribute to the committee's overall goal of building research capacity in inter-professional education and collaboration. A framework provides a structured approach to identifying research and evaluation priorities and objectives. Furthermore, the process of applying the framework engages the committee members in determining the course of action. The process can be easily transferred to other areas in need of research capacity building.
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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 | 0.193 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.020 | 0.059 |
| Scholarly communication | 0.026 | 0.017 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".