Stakeholder-Based Methods to Develop a Toolkit to Promote Engagement in Assisted Living Safety
Bibliographic record
Abstract
Abstract This presentation provides and overview of a mixed-methods stakeholder engaged study to develop a toolkit to encourage resident and family engagement in the safety of assisted living (AL). This study uses stakeholder-based data and stakeholder engaged processes to adapt existing tools and strategies from other settings to encourage resident and family engagement in the safety of AL. We will improve resident safety in AL by developing an evidence-based tool to implement these engagement tools/strategies in AL. The presentation will outline the theoretical base, the approach for this study, including efforts to recruit and retain stakeholders throughout the study, and stakeholder engaged process to develop the toolkit. The presentation will include challenges and strategies to encourage participation of AL staff, residents, and family caregivers in the study. The presentation will conclude with a discussion of implications for future design and research efforts aiming to impact AL care, policy, and research implementation.
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 | 0.136 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".