Patient engagement in Canada: a scoping review of the ‘how’ and ‘what’ of patient engagement in health research
Bibliographic record
Abstract
BACKGROUND: Over the last 10 years, patient engagement in health research has emerged as the next evolution in healthcare research. However, limited evidence about the clear role and scope of patient engagement in health research and a lack of evidence about its impact have influenced the uptake, implementation and ongoing evolution of patient engagement. The present study aims to conduct a scoping review to identify methods for and outcomes of patient engagement in health research. METHODS: An adaptation of the scoping review methodology originally described by Arksey and O'Malley and updated by Levac, Colquhoun and O'Brien was applied. Sources from a formal database search and relevant documents from a grey literature search were compiled into data extraction tables. Articles were synthesised into key themes according to the (1) methods and (2) outcomes of patient engagement in health research. RESULTS: The total yield for the scoping review was 55 records from across Canada, the United Kingdom and the United States. While evidence about the methods used to engage patients in health research is increasing, stronger evidence of specific patient and healthcare system outcomes is required. This necessitates further mobilisation of research that explores outcomes and that validates specific tools to evaluate engagement. Additionally, theoretical frameworks that can better inform and sustain patient engagement across the lifecycle of health research are lacking. CONCLUSION: Further increasing the volume and reach of evidence about patient engagement in health research will support the paradigmatic shift needed to normalise the patient's role in research beyond 'subject' or 'participant', so as to ultimately improve patient health outcomes and better address healthcare reform in Canada.
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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 | 0.118 | 0.288 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.036 | 0.063 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| 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".