Developing a toolkit to improve resident and family engagement in the safety of assisted living: Engage—A stakeholder‐engaged research protocol
Bibliographic record
Abstract
Assisted living (AL) communities are experiencing rising levels of resident acuity, challenging efforts to balance person-centered care-which prioritizes personhood, autonomy, and relationship-based care practices-with efforts to keep residents safe. Safety is a broad-scale problem in AL that encompasses care concerns (e.g., abuse/neglect, medication errors, inadequate staffing, and infection management) as well as resident issues (e.g., falls, elopement, and medical emergencies). Person and family engagement (PFE) is one approach to achieving a balance between person-centered care and safety. In other settings, PFE interventions have improved patient care processes, outcomes, and experiences. In this paper, we describe the protocol for a multiple methods AHRQ-funded study (Engage) to develop a toolkit for increasing resident and family engagement in AL safety. The study aims are to engage AL residents and family caregivers, AL staff, and other AL stakeholders to (1) identify common AL safety problems; (2) prioritize safety problems and identify and evaluate existing PFE interventions with the potential to address safety problems in the AL setting; and (3) develop a testable toolkit to improve PFE in AL safety. We discuss our methods, including qualitative interviews, a scoping review of existing PFE interventions, and stakeholder panel meetings that involved a Delphi priority-setting exercise. In addition to describing the protocol, we detail how we modified the protocol to address the unique challenges of the COVID-19 pandemic. Study findings will result in a toolkit to improve resident and family engagement in the safety of AL that will be tested in future research.
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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 | 0.107 | 0.091 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.065 | 0.013 |
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".