Complex skills are required for new primary health care researchers: a training program responds
Bibliographic record
Abstract
Abstract Background Current dimensions of the primary health care research (PHC) context, including the need for contextualized research methods to address complex questions, and the co-creation of knowledge through partnerships with stakeholders – require PHC researchers to have a comprehensive set of skills for engaging effectively in high impact research. Main body In 2002 we developed a unique program to respond to these needs - Transdisciplinary Understanding and Training on Research - Primary Health Care (TUTOR-PHC). The program’s goals are to train a cadre of PHC researchers, clinicians, and decision makers in interdisciplinary research to aid them in tackling current and future challenges in PHC and in leading collaborative interdisciplinary research teams. Seven essential educational approaches employed by TUTOR-PHC are described, as well as the principles underlying the curriculum. This program is unique because of its pan-Canadian nature, longevity, and the multiplicity of disciplines represented. Program evaluation results indicate: 1) overall program experiences are very positive; 2) TUTOR-PHC increases trainee interdisciplinary research understanding and activity; and 3) this training assists in developing their interdisciplinary research careers. Taken together, the structure of the program, its content, educational approaches, and principles, represent a complex whole. This complexity parallels that of the PHC research context – a context that requires researchers who are able to respond to multiple challenges. Conclusion We present this description of ways to teach and learn the advanced complex skills necessary for successful PHC researchers with a view to supporting the potential uptake of program components in other settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".