FORMATIVE EVALUATION IN THE IMPLEMENTATION OF THE HOME SAFETY TOOLKIT FOR VETERANS WITH DEMENTIA
Bibliographic record
Abstract
Objective: To identify the challenges faced by CGs prior, during, and following the transition to LTC that address access to services.Methods: Caregiver -person with dementia (PWD) dyads were identified from a larger prospective study that occurred over a period of 18 months.Semistructured interviews were designed to appraise the CG's situation over time.A total of 25 participants initiated the process of relocating to LTC, and 7 were placed in a LTC facility by the end of study.Interview data was thematically organized, coded, and categorized.Results: Participants' experiences were grouped under stages in the timeline of relocation to LTC.Initially, relocation was motivated by increasing CG burden and an awareness of the PWD's health decline.As the dyads advanced in the trajectory, the decision making process was complicated by challenges in predicting the dementia trajectory and a marked concern about waiting lists.Finally, post-move, and despite relocation, CGs continued to monitor PWD's care and health decline.The results are discussed within the context of strategies for facilitating transitions that address systemic barriers and access to information, thereby adding to the already rich literature on the emotional adaptation to relocation.
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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 | 0.085 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".