Scoping review of methods for engaging long-term care residents living with dementia in research and guideline development
Bibliographic record
Abstract
OBJECTIVES: To describe: (1) methods used to engage long-term care (LTC) residents living with dementia in research and guideline development; (2) the outcomes of engagement; and (3) barriers and facilitators to engagement. DESIGN: Scoping review. SEARCH STRATEGY: We conducted searches in Academic Search Premier (EBSCO), APA PsychInfo (EBSCO), CINAHL (EBSCO), Medline (OVID), Embase (Elsevier), Web of Science and the Cochrane database, and a structured grey literature search in July 2021 and updated in March 2023. We included studies that described or evaluated resident engagement, defined as including residents living with dementia in the process of developing healthcare guidelines or research which could include collaborators or partners in planning, execution or dissemination of the guideline or research. Title, abstracts and full-texts were screened for eligibility by two team members using a pilot-tested process. Data were extracted from included studies independently and in duplicate by two team members using a pre-tested data extraction form. Results were narratively synthesised according to the research question they addressed. RESULTS: We identified three studies for inclusion. Residents were engaged at the beginning of the research projects through interviews, focus groups, and consultations. None of the included articles described the outcomes of engagement. Barriers to engagement were predominantly at the resident level, including impaired verbal communication limiting resident's abilities to participate in discussions, while increased time to support engagement was reported as a barrier at the resident and research team levels. CONCLUSIONS: We found a small body of literature describing the engagement of LTC residents in health research and guideline development. Future work should explore alternative methods to engage LTC residents living with dementia, including art-based methods, and the effect of including resident engagement. Guideline developers and researchers should ensure adequate time and human resources are allocated to support engagement.
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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 | 0.106 | 0.284 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.053 | 0.045 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".