”I felt there was a big chunk taken out of my life”: COVID-19 and older adults’ library-based magazine leisure reading
Bibliographic record
Abstract
Reading is a central leisure activity among older adults, serving as a means of entertainment, escape, connection, and/or education. COVID-19 public library closures drastically altered this activity. Based on interviews with 21 older adults across Ontario, Canada, this study explores how library closures in the province affected older adults’ magazine leisure reading practices. Analysis yielded three themes: COVID-19 transforming experiences of library as place, COVID-19 as time of loss, and COVID-19 as catalyst for adaptation. Participants voiced the many ways COVID-19 has shaped (often restricting) their choices related to magazine reading (where, how, and what they read, and where they located their magazines). While libraries remained virtually open during the pandemic, many participants chose not to switch to digital platforms (despite their technical proficiency to do so). As a result, they often stopped reading magazines completely, despite the loss this stoppage represented. At the same time, pandemic restrictions compelled others to use the online library services they had previously avoided. Ultimately, participants’ experiences of magazine reading during the COVID-19 pandemic further our understanding of reading as leisure in later life and also trouble prevailing assumptions that older adults’ resistance to digital media engagement is merely a reflection of age-related incompetence.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".