Impact of Social Media on Health-Related Outcomes Among Older Adults in Singapore: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The worldwide spread of digitalization has led to the harnessing of technology to improve health outcomes. Paying attention to older adults' social needs via social media is one way to promote healthy aging. Although 56% of older adults are smartphone users, little is known about their use patterns of social media. OBJECTIVE: This exploratory study aims to determine the experiences of social media apps' use among older adults in Singapore and understand their perceptions of its impact on health-related outcomes. METHODS: This study used a qualitative research design with an interpretative approach. Using maximum variation purposive sampling, normal aging older adults (N=16) who were aged between 60 and 80 years and experienced in the use of internet-enabled technology were recruited from an existing community study. Semistructured, in-depth interviews were conducted. Employing a thematic analysis, interviews were transcribed verbatim and analyzed for codes inductively. RESULTS: The following themes and subthemes were identified as key moderators of older adults' experiences on social media apps: (1) personal attitudes: participants were encouraged to use social media due to the increased accessibility, which enabled the ease of contact, but perceptions that the quality of interactions was compromised and its associated risks reduced their use; and (2) social influences: the desire to bond with co-users and the availability of support increased use. In addition, use of social media apps was perceived to positively impact health through its ability to keep older adults cognitively engaged, improve health communication, and increase social connectedness. However, opinions remained mixed on older adults' vulnerability to social media addiction. CONCLUSIONS: Personal and social contexts determine older adults' social media use. This study's findings provide practical insights into how social media can be deployed to improve health-related outcomes in older adults.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".