Listened to but Rarely Heard: A Scoping Review of Resident Engagement in the Organizational Design and Governance of their Long-Term Care Homes
Bibliographic record
Abstract
Abstract Engaging residents of long-term care homes (LTCHs) in their home’s environment, programs, and operations is required in some jurisdictions and could improve resident quality of life and other outcomes. This scoping review summarized existing research on resident engagement in LTCH organizational design and governance, including associated enablers, barriers, approaches, and outcomes. The database search yielded 5,580 records (after deduplication), and 62 articles covering 59 studies were included. These studies predominantly described Residents’ Councils ( n = 38; 64%) and enablers or barriers pertaining to resident and home perspectives, as well as implementation and sustainability infrastructure. Few studies described approaches to considerations of resident diversity ( n = 8; 14%) or the presence of dementia and/or cognitive impairment ( n = 12; 20%). Ten studies reported quantitative data evaluating resident engagement, and only four with resident-reported outcomes. Robust, evidence-informed frameworks that are co-designed with residents, staff, and others in the LTCH sector are needed to engage residents in their LTCHs.
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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".