Innovation in collaborative health research training: The role of active learning
Bibliographic record
Abstract
This paper describes and discusses the essential pedagogical elements of the Partnering in Community Health Research (PCHR) program, which was designed to address the training needs of researchers who participate in collaborative, interdisciplinary health research. These elements were intended to foster specific skills that helped learners develop research partnerships featuring knowledge, capabilities, values and attitudes needed for successful research projects. By establishing research teams called "clusters", PCHR provided research training and experience for graduate students and post-doctoral fellows, as well as for community health workers and professionals. Pedagogical elements relied on active learning approaches such as inquiry-based and experience-based learning. Links between these elements and learning approaches are explained. Through their work in cluster-based applied research projects, the development of learning plans, and cross-cluster learning events, trainees acquired collaborative research competencies that were valuable, relevant and theoretically informed.
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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 | 0.042 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".