FOSTERING HOMELIKENESS IN NURSING HOMES: QUALITATIVE RESULTS FROM FAMILY AND FRIENDS OF RESIDENTS
Bibliographic record
Abstract
Identifying how to enhance the quality of life for older adults living in nursing homes can contribute to transforming health care institutions into person-centered homes, an approach to care that places residents’ relationships, life experiences, abilities, preferences and dignity at the forefront. One component of a federally-funded study of 23 nursing homes in Nova Scotia, Canada, focused on how to enhance the quality of life in nursing homes from the perspective of the family members and friends of the residents. Quantitative results clearly showed that from their perspective, homelikeness is associated with higher resident quality of life. We will present qualitative results to provide further insights into how to foster homelikeness within the nursing home environment. We thematically analyzed data collected from family members and friends of nursing home residents through 1) open-ended survey questions from 397 family members and friends, and 2) focus groups with 20 family members and friends who participated in the survey. Analysis of open-ended survey questions resulted in identifying key features that either strengthen or limit homelikeness in nursing homes. Analysis of the focus group data resulted in further identifying how homelikeness can be fostered in three key ways: care provided and relationships (e.g. staffing models that allow for individualized care), public spaces (e.g. the effective use of public spaces to support relationships), and private spaces (e.g. personalization). Our results provide evidence to nursing home decision makers about how to improve resident quality of life through creating a homelike environment.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".