Giving patients a voice: a participatory evaluation of patient engagement in Newfoundland and Labrador Health Research
Bibliographic record
Abstract
BACKGROUND: Efforts to engage patients as partners in health research have grown and thereby the need for feedback and evaluation. In this pilot evaluation study, we aimed to 1) evaluate patient engagement in health research projects in Newfoundland and Labrador, Canada, and 2) learn more about how to best monitor and evaluate patient engagement. This paper presents the results of our participatory evaluation study and the lessons learned. The evaluation of the projects was driven by questions patients wanted answered. METHODS: = 13) working on their own research projects participated. Participants completed an online survey with closed and open-ended questions about their patient engagement efforts, experiences and preliminary outcomes. Patients were involved as co-investigators in the entire evaluation study. We used qualitative methods to evaluate our participatory process. RESULTS: The evaluation study results show that most patients and researchers felt prepared and worked together in various phases of the research process. Both groups felt that the insights and comments of patients influenced research decisions. They believed that patient engagement improved the quality and uptake of research. Students felt less prepared and were less satisfied with their patient engagement experience compared to researchers and their patient partners. Involvement of patient co-investigators in this evaluation resulted in learnings, transparency, validation of findings and increased applicability. Challenges were to select evaluation questions relevant to all stakeholders and to adapt evaluation tools to local needs. CONCLUSIONS: Our findings show that researchers, patient partners and students value patient engagement in health research. Capacity building at the supervisor level in academic institutions is needed to better support students. Sufficient time is also needed to permit observable outcomes. Participatory evaluation may increase the relevance and usefulness of information, but it also raises issues such as who defines and designs the content of evaluation tools. A co-creation process is required to develop appropriate monitoring and evaluation strategies.
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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 | 0.190 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".