Assessing and Improving Processes and Outcomes of the McGill Primary Health Care Research Network (summer bursary)
Bibliographic record
Abstract
Background: The McGill Primary Health Care Research Network (the Network) is a Practice-Based Research Network (PBRN) that promotes the collaboration between researchers and clinicians in research. The Network follows an approach called Organizational Participatory Research (OPR).Purpose: To discover the processes and outcomes associated with the Network, to learn about researcher and clinician collaboration within the Network and to propose recommendations and a revised questionnaire.Methods: A thematic qualitative data analysis was conducted. The data that was analyzed consisted of the diaries of two diaries of two Network coordinators, email correspondence between the core group members and the coordinators, and the minutes of 12 core group meetings. The data were interpreted according to the Capacity Building Framework. Then, codes were organized according to 10 framework-based meta-themes (5 domain-related processes, and 5 domain-related outcomes) and grouped in 24 key-themes (key processes and outcomes).Results: A leadership process was researchers promoting communication within the Network which resulted in clinicians becoming project leaders. An organizational development outcome was members' research projects being completed. Partnership processes involved researchers and clinicians identifying their respective challenges to partnerships. The main outcome was collaboration. Resource allocation processes included time and funding management, with accommodation to time constraints as an outcome. Workforce development processes included researchers educating clinicians, which resulted in networking core group members and positive learning experiences. Conclusions: The results suggested practice and policy recommendations. Based on the results, an improved mailing policy, a wiki/blog, a more humble approach for researchers and a questionnaire were proposed.
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 | 0.186 | 0.310 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".