Scoping review of models and frameworks of patient engagement in health services research
Bibliographic record
Abstract
OBJECTIVE: To count and describe the elements that overlap (ie, present in two or more) and diverge between models and frameworks of patient engagement in health services research. Our specific research question was 'what are the elements that underlie models and frameworks of patient engagement in health services research?' DESIGN: Scoping review. DATA SOURCES: On 6-7 July 2021, we searched six electronic databases (ie, CINAHL, Cochrane Database of Systematic Reviews, Joanna Briggs Institute Evidence Based Practice Database, MEDLINE, PsycINFO and Scopus) and Google Scholar for published literature, and ProQuest Dissertations & Theses, Conference Proceedings Citation Index, Google, and key agencies' websites for unpublished (ie, grey) literature, with no date restrictions. These searches were supplemented by snowball sampling. ELIGIBILITY CRITERIA: We included published and unpublished literature that presented (a) models or frameworks (b) of patient engagement (c) in health services research. We excluded articles unavailable as full text or not written in English. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers extracted data from included articles using an a priori developed standardised form. Data were synthesised using both quantitative (ie, counts) and qualitative (ie, mapping) analyses. RESULTS: We identified a total of 8069 articles and ultimately included 14 models and frameworks in the review. These models and frameworks were comprised of 18 overlapping and 57 diverging elements, that were organised into six conceptual categories (ie, principles, foundational components, contexts, actions, levels and outcomes) and spanned intrapersonal, interpersonal, process, environmental, and health systems and outcomes domains. CONCLUSIONS: There is little overlap between the elements that comprise existing models and frameworks of patient engagement in health services research. Those seeking to apply these models and frameworks should consider the 'fit' of each element, by conceptual category and domain, within the context of their study.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".