Older Person’s Participation in Life-Enhancement Activities: A Mixed-Methods Observational Study
Bibliographic record
Abstract
Abstract This study examines older persons’ experiences and participation in life-enhancement activities in a Canadian long-term care (LTC) facility. Naturalistic observations of 20 life-enhancement activity sessions were conducted in a single LTC facility that includes 111 older persons in September 2024. Data were collected through field notes and guiding questions for systematic observation. NVivo software (version 1.7.1) was used for qualitative thematic analysis, and SPSS software (version 30.0) was used for quantitative analysis. We used Kruskal-Wallis and Mann-Whitney U tests for analysis, with results considered significant if p < 0.05. There were significant differences in engagement levels: self-initiative (p < 0.001), assistance-seeking frequency (p = 0.002), and social interaction frequency (p < 0.001) across activities. Social interaction frequency also significantly differed by mobility status (wheelchair, walker, independent) (p = 0.014). Interpersonal conflict incidents (p < 0.001) and positive emotional expressions (p < 0.001) varied across activity locations (TV room versus Activity Room). Participants displayed significantly more distractions in the TV Room than in the Activity Room (p = 0.001). Four themes emerged from thematic analysis: 1) participation barriers, 2) activity contextual factors, 3) facilitator support strategies, and 4) social interactions and emotional well-being. Individual (mobility) and situational (activity location) factors can influence older persons’ engagement in life-enhancement activities. For older persons to achieve active participation in life-enhancement activities, we must consider their mobility needs, a non-disruptive environment, and facilitator support. Life enhancement activities benefit from facilitators promoting independence and teamwork, improving participation and social connections.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| 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".