BETTER LIVING THROUGH TECH: TECHNOLOGY-MEDIATED RECREATION AND LONG-TERM CARE FACILITY RESIDENTS’ QUALITY OF LIFE
Bibliographic record
Abstract
Abstract Empirical research on long-term care facility resident engagement has consistently indicated that increased engagement is associated with more positive clinical outcomes and increased quality of life. The current study adds to this existing literature by documenting the positive effects of technologically-mediated recreational programing on quality of life and medication usage in aged residents living in long-term care facilities. Technologically-mediated recreational programming was defined as recreational programming that was developed, implemented, and /or monitored using software platforms dedicated specifically for these types of activities. This study utilized a longitudinal design and was part of a larger project examining quality of life in older adults. A sample of 272 residents from three long-term care facilities in Toronto, Ontario participated in this project. Resident quality of life was assessed at multiple time points across a span of approximately 12 months, and resident engagement in recreational programming was monitored continuously during this twelve-month period. Quality of life was measured using the Resident Assessment Instrument Minimum Data Set Version 2.0. Number of pharmacological medication prescriptions received during the twelve-month study period was also assessed. Descriptive analyses indicated that, in general, resident functioning tended to decrease over time. However, when controlling for age, gender, and baseline measures of resident functioning, engagement in technologically-mediated recreational programming was positively associated with several indicators of quality of life. The current findings thus indicate that engagement in technology-mediated recreational programming is associated with increased quality of life of residents in long-term care 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".