Transforming the health research system: Embedding patient engagement in decision-making
Bibliographic record
Abstract
This thesis starts from the societal problem of public mistrust in science and research waste, and thereby the need to change the health research system from a supply-oriented towards a needs-oriented system through engaging patients. This thesis aims to understand and enhance the embedding of meaningful patient engagement in health research decision-making processes. The two initiatives described in this thesis, (1) Canada's Strategy for Patient-Oriented Research (SPOR) and (2) Patients Active In Research and Dialogues for an Improved Generation of Medicine (PARADIGM), provided insights into strategies actors can employ to enable and advance patient engagement. We identified and implemented three enabling strategies for system change: (1) Matchmaking support for connecting actors (Chapter 4) (2) Training to facilitate competence building (Chapter 5) (3) Monitoring and evaluation to facilitate collective learning (Chapter 6, 7, 8, 9) As part of these strategies, we built infrastructures to support patient engagement, such as recruitment services and training facilities, as well as a monitoring and evaluation (M&E) framework, as a stepping stone towards an M&E facility. As a result, the perspectives and attitudes of participating actors towards experiential knowledge and the value of patient engagement changed. In practice, new relationships were formed, competencies improved, and new research practices in which patients are partners emerged. Furthermore, the Patient Engagement Monitoring and Evaluation Framework developed as part of this thesis provides a structured way to facilitate conversations among diverse stakeholders about the objectives of patient engagement, the process of change, and meaningful metrics. Participatory evaluation approaches stimulated collective learning on the practice and the shared value of patient engagement. This thesis concludes with suggestions of strategies to further facilitate the embedding of patient engagement and possible metrics to track change over time (Chapter 10). Through the research conducted for this thesis we aimed to inspire and support the shift towards a more needs-oriented health research system. By collaboratively changing the research culture, structure and practice, we can create a system that co-produces knowledge that can be used for better health.
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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 | 0.116 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.043 |
| Scholarly communication | 0.036 | 0.021 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".