Development and Implementation of Training for Interdisciplinary Research in Primary Health Care
Bibliographic record
Abstract
The authors describe a national training program in Canada focusing on research in primary health care (PHC). The program, sponsored by the Canadian Institutes of Health Research's Strategic Training in Health Research Program, is called Transdisciplinary Understanding and Training on Research-Primary Health Care (TUTOR-PHC); it began in 2002 and is funded to continue until 2015. The purpose-built curriculum has two main goals: (1) to build a cadre of skilled, independent researchers to enhance the evidence base for PHC practice and policy and (2) to increase the interdisciplinary focus in PHC research. The program consists of three elements: (1) a three-day on-site symposium, (2) four online workshops (three weeks each), and (3) two online interdisciplinary discussion groups (seven weeks each). Participants develop PHC research skills during in-person and online workshops. They gain knowledge of and experience in interdisciplinary PHC research through participation in interdisciplinary discussion groups and by observing mentor interactions. Both the symposium and the online components involve a variety of interactive education approaches. The 77 graduates from across Canada represent 14 disciplines, most commonly family medicine, nursing, epidemiology, psychology, social work, and sociology. Graduates of the program publish at a high rate and are building their careers in PHC research. The structure of TUTOR-PHC encourages not only skill development and content uptake but also the exchange of tacit knowledge. The complete program leads to a synthesis of skills, knowledge, personal communication abilities, and cross-discipline curiosity, creating a well-rounded collaborative PHC researcher.
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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 | 0.063 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".