Online Leisure Activities for Sustained Mental Health Well-being in Older Adults with COVID-19 Mitigation
Bibliographic record
Abstract
Abstract Older adults (OA) experience psychosocial distress from the COVID-19 pandemic mitigations. While their participation in leisure and recreation activities (LRA) would be ameliorating, we do not know how LRA OA engages for their mental health (MH) well-being with COVID-19 mitigation. This scoping review aimed to trend the evidence on the types of LRA OA engage for their MH well-being across the young-old continuum (60-69 years) through to older-old (80 years and above) in the COVID-19 pandemic. We searched the following electronic databases (PubMed, EMBASE, CINAHL, Cochrane, JBI-ES, and Epistemonicos for LRA studies by OA with COVID-19 mitigation. To be included, we considered empirical articles published in English on LRA of OA 55+ years-old. Another criterion required articles describing those activities' qualities and the impact of LRA on MH and well-being during the COVID-19 pandemic. We resulted in seven empirical studies, two of which implemented in the USA and one from the USA and Canada, Spain, Israel, and Japan. Findings following narrative synthesis revealed trending evidence on OA to engage in online LRA for social, cognitive /intellectual, and emotional health. Leisure-time physical activity reduced negative MH symptoms as anxiety and depression in OA under COVID-19 threat. In conclusion, the present review's trending evidence suggests that OA engagement in social, physical, mental, and cognitive LRA enhanced their MH and overall well-being. Activities delivered by way of the Internet and television provided a cluster of beneficial opportunities for the OA mental health needs under the COVID-19 pandemic.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".