Models and frameworks of patient engagement in health services research: a scoping review protocol
Bibliographic record
Abstract
Plain English summary Patient engagement in research is an emerging approach that involves active and meaningful collaboration between researchers and patients throughout all phases of a project, including planning, data collection and analysis, and sharing of findings. To better understand the core features (elements) that underlie patient engagement, it is useful to have a look at models and frameworks that guide its conduct. Therefore, this manuscript aims to present a protocol for a scoping review of models and frameworks of patient engagement in health services research. Methods: Our protocol design is based on an established framework for conducting scoping reviews. We will identify relevant models and frameworks through systematic searches of electronic databases, websites, reference lists of included articles, and correspondence with colleagues and experts. We will include published and unpublished articles that present models and frameworks of patient engagement in health services research and exclude those not in English or unavailable as full texts. Two reviewers will independently review abstracts and full texts of identified articles for inclusion and extract relevant data; a third reviewer will resolve discrepancies. Our primary objective is to count and describe elements of patient engagement that overlap (present in 2 or more) and diverge among included models and frameworks. Discussion: We hope this review will raise awareness of existing models and frameworks of patient engagement in health services research. Further, by identifying elements that overlap and diverge between models and frameworks, this review will contribute to a clearer understanding of what patient engagement in research is and/or could be. Abstract Background: Patients can bring an expert voice to healthcare research through their lived experience of receiving healthcare services. Patient engagement in research is an emerging approach that challenges researchers to acknowledge and utilize this expertise through meaningful and active collaboration with patients throughout the research process. In order to facilitate a clearer understanding of the core elements that underlie patient engagement, it is useful to examine existing models and frameworks that guide its conduct. Therefore, the aim of this manuscript is to present a protocol for a scoping review of models and frameworks of patient engagement in health services research. Methods: Drawing on Arksey and O’Malley’s and Levac et al.’s framework for scoping reviews, we designed our protocol to identify relevant a) published articles through systematic searches of 7 electronic databases and snowball sampling and b) unpublished articles through systematic searches of databases and websites and snowball sampling. We will include published and unpublished models and frameworks of patient engagement in health services research and exclude those not in English or unavailable as full texts. Two reviewers will independently screen the abstracts and full texts of identified articles for inclusion and extract relevant data; a third reviewer will resolve disagreements. We will conduct a descriptive analysis of the characteristics (i.e., elements underlying patient engagement and those related to the study authors, publication, and model/framework) of included articles and a narrative analysis of the data concerning elements of the model or framework. Our primary objective is to count and describe elements of patient engagement that overlap (present in ≥ 2) and diverge (present in < 2) among identified models and frameworks. Discussion: Through identification of elements that overlap and diverge between existing models and frameworks, this review will provide a starting point for the critical reflection on our collective understanding of what patient engagement in health services research is and/or could be. Ultimately, we hope that the findings of this review raise awareness of existing models and frameworks and shed light on some of the complexity of conducting patient engaged research through identification of key elements that shape this approach.
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 | 0.281 | 0.247 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.056 | 0.015 |
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".