Does SNS Usage by Older Adults Reduce Loneliness?
Bibliographic record
Abstract
As the use of social networking sites (SNSs) has become more wide-spread, some age groups have taken to the media much more readily than other groups. Older adults are lagging behind in their adoption of SNSs, while this group of the population tends to be more socially isolated and lonely. In this thesis, the uses of SNSs have been broken down into different components such as the intimacy level of the message content, types of contacts, etc. A framework for social capital is utilized, in order to bridge the knowledge gap between how older adults use social networking sites to gauge its impact on loneliness. The findings suggest that the use of SNSs increases social capital but does not directly reduce loneliness. The impact of the increase of social capital by using SNSs on loneliness is negligible. However, increased social capital due to SNSs use tends to moderate the effects that health status, financial wellbeing and satisfaction with offline relationships have on loneliness.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".