Evaluating the impacts of patient engagement on a national health research network: results of a case study of the Chronic Pain Network
Bibliographic record
Abstract
Abstract Background The Chronic Pain Network (CPN) is a pan-Canadian research network focused on innovating and improving the quality and delivery of pain prevention, assessment, management and research for all Canadians. An important focus of the CPN is to work in collaboration with patient partners. Patient partners, researchers and clinicians work together in all aspects of the research network including on funded research projects and in the governance of the Network. Given this focus, the CPN identified the importance of evaluating their patient engagement work to understand its functioning and impact. Methods The objective of this exploratory evaluation case study was to understand the impacts of patient engagement on the CPN. The CPN worked with an external evaluation team which established an arms-length approach to the evaluation. Interviews were conducted with CPN members, including patient partners, leadership, funded researchers and committee co-chairs, at three discrete time points to trace the evolution of the patient engagement program within the Network. Key Network documents were also collected and reviewed. Data were analyzed following each set of interviews using content analysis guided by the principles of constant comparison and qualitative description. A final round of analysis was conducted using the Engage with Impact Toolkit, an impact measurement framework, to identify impacts of engagement. Results Impacts of patient engagement were identified at the individual, network, funded research project and research community levels. These impacts were observed in the following areas: (1) building community; (2) developing knowledge, skills and resources; (3) increasing confidence; (4) influencing priorities and decisions; (5) enabling additional opportunities; (6) promoting culture change; and, (7) coping with experiences of living with chronic pain. Conclusions While not without challenges, the patient engagement efforts of the CPN demonstrates the impact engaging patient partners can have on a national research network and related policy activities. Understanding the approaches to, and impacts of, patient engagement on health research networks can illuminate the value of having patient partners engaged in all aspects of a research network and should serve as encouragement to others who look to take on similar work.
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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 | 0.074 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".