Average or extraordinary? A tale of two studied samples’ anxiety related recovery work after COVID-19
Bibliographic record
Abstract
Introduction A global pandemic is a hardship and mentally distressing event for any of us, and particularly for people living at a greater risk of post-infectious health harms. Public discourse about COVID-19 largely characterizes older people as a physically and mentally vulnerable demographic. Research findings largely now to the contrary consider age an asset, a perspective in keeping with Seligman’s idea that everyday people can also see the positive side of life and act accordingly when faced with events that are neither positive nor within their control. With this in mind, we explore how average older people were managing pandemic-related anxiety when mandated COVID-19 public health measures were lifted. Methods Our primary study sample was a national census-based quota sample (N = 1,327) of average older Canadian people. A second study sample was recruited by convenience (N = 1,200) for comparison purposes. Both groups responded to an e-survey launched between July 1st and up to August 16th, 2022, about how anxious they felt and how they were managing at this key turning point. Results Convenience sample responders were largely residing in Ontario (Z = 781.667, p < 0.001), in very good to excellent health (Z = 180.534, p < 0.001), and university educated (Z = 1285.255, p < 0.001). Far fewer were in their 60s (Z = 124.898, p < 0.001; Z = 22.349, p < 0.001). Descriptive network analyses revealed that the two studied samples had in common a diverse and purposive network of coping strategies for managing pandemic-related anxiety. Discussion Average older Canadians managed their anxiety as capably as healthier, better educated, and generally older peers. Our findings are explored through a lens of positivity, not vulnerability. Methodological provocations are offered for future research, including post-pandemic between-sampling comparisons.
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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 | 0.002 | 0.007 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| 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".