Connecting Through Leisure: A Multi-author Blog as A Site for Relationship-building During the Transition to Retirement
Bibliographic record
Abstract
Leisure is vital for social engagement, whether in-person or virtual. Older adults may be at risk of social isolation and loneliness, yet social engagement may counteract the negative implications of this for quality of life. In this article, we share findings from our longitudinal online qualitative study exploring leisure and retirement transitions. In particular, we focus on the role of a multi-author blog in creating opportunities for our study participants to engage with one another, connect over shared leisure interests, and build a safe and supportive online community. We collected data from 44 baby boomers over three years. Using reflexive thematic analysis, we generated two main themes: creating a sense of community, and blogging as a space for mutual support. Community was fostered through humour, shared interests, and resource sharing. As a sense of community formed, the blog evolved into space for mutual support where participants cheered each other on, inspired each other, sought and offered advice, and supported one another during challenging times. Our findings demonstrate that a multi-author blog not only serves as a valuable tool for collecting data about leisure experiences, but also benefits research participants by facilitating common ground and fostering a supportive community. This community allows for sharing of ideas and knowledge, celebration of successes, and compassion during difficulties. Its theoretical and practical implications are further discussed.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".