Quality of life in assisted living facilities for seniors: A descriptive exploratory study
Bibliographic record
Abstract
INTRODUCTION: Promoting individuals' health across different life spans has always been key to a holistic nursing practice. Seniors are a diverse population who go through many physical and mental changes as they age. During the last decade, assisted living facilities (ALFs) have dramatically increased in numbers to provide care and living services in a home-like environment. AIM: The aim of this descriptive exploratory study was to explore the quality of life as perceived by seniors who reside in assisted living facilities (ALFs). DESIGN: This study utilized a descriptive exploratory design to investigate the quality of life of seniors living in ALFs. METHODS: Seventeen residents from two ALFs were interviewed to gather their perspectives on the quality of their lives while living in an ALF. The interviews were conducted by the researcher and were audio-recorded and transcribed verbatim. The data were analysed using thematic analysis. RESULTS: Three major themes surfaced from residents' descriptions: 'physical environment', 'social environment' and 'home-like atmosphere'. The quality of life in ALFs was found to be predominantly an outcome of the exchange between the personal capability of residents to adapt to changes and the capacity of the facility to meet residents' diverse needs. PATIENT OR PUBLIC CONTRIBUTION: Participants who discussed their quality of life in ALFs provided profound insights into this aspect of their lives. The findings from this study can potentially enlighten ALF stakeholders and enhance the quality of life for seniors residing in these facilities.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".