IDENTIFYING ASSISTED LIVING SAFETY PRIORITIES: A DELPHI PANEL WITH RESIDENT, FAMILY, AND PROFESSIONAL STAKEHOLDERS
Bibliographic record
Abstract
Abstract While assisted living (AL) communities emphasize resident safety, one barrier to resident safety is a lack of information about AL stakeholders’ safety priorities. As part of a larger research project to create a toolkit to foster resident and family engagement in safety in AL, we created a stakeholder panel that includes 13 AL residents, family members, and professionals (i.e., direct care workers, administrators, researchers, and policymakers). This paper describes a web-based Delphi process to create a ranked safety priority list with stakeholders. After three rounds involving online surveys and Zoom group discussions, the stakeholder group came to a consensus on a final list of 14 ranked safety priorities. Using verbatim transcripts of the Zoom discussions and chat, we conducted content analysis to highlight the rationales for the 14 ranked safety priorities on the final list. Reasons to prioritize safety concerns included the seriousness of the impact on AL residents and system-level root causes. These findings will be used to guide the development of a toolkit to improve resident and family engagement in the safety of AL. This list can also help AL communities and researchers at large to better understand what safety priorities are most important to a broad range of AL stakeholders and why.
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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 | 0.094 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".