Understanding the Experiences of COVID-19 Public Health Measures and Well-Being: A Qualitative Study Among Older Adults in Quebec, Canada
Bibliographic record
Abstract
This interpretative descriptive study explores how public health measures implemented during the first wave of the COVID-19 pandemic in Quebec, Canada, affected the well-being of older adults. Twenty-six participants aged 60-81 took photographs to depict how COVID-19 public health measures affected their well-being and were invited to discuss their photographs in virtual focus groups. Data were analyzed using thematic analysis. The impacts of health measures on the well-being of participants were framed according to three overarching themes. First, participants endured an intensification of ageism, feeling diminished and excluded from their social spheres. Second, they faced a burden of loneliness due to the loss of connections with their communities, particularly for those who were single and without children. Third, participants highlighted navigating a degradation of social cohesion. This manifested through tensions and distrust in both the public and private spheres, as well as acts of resistance in response to rules deemed unjust. While public health measures were essential to prevent onward transmission of COVID-19 and mortality, they negatively impacted older adults' self-image, loneliness, and trust in society. This study argues for a rethinking of public health norms specific to older adults to address potential sources of inequality. In particular, a greater emphasis is needed on social connectedness and addressing the unique needs of older adults during pandemics.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".