Finding Positives Amidst the Negatives: A Thematic Analysis of the Impact of the COVID-19 Pandemic on Canadians 55+
Bibliographic record
Abstract
Disasters such as the COVID-19 pandemic exact a toll on vulnerable populations in terms of morbidity and mortality, but they also provide opportunities for personal growth and development and demonstration of personal and collective resiliency. This inductive thematic analysis explores self-perceived negative and positive impacts of the COVID-19 pandemic on 2994 Canadians aged 55+. Data derive from response to two open-ended questions included in a national online survey (View Survey (sfu.ca)) conducted between August-October 2020. Respondents were recruited using Facebook, and a widespread email campaign to organizations serving older adults. 4260 of the 6573 coded comments (66%) addressed negative impacts of COVID-19. Fewer but still a considerable number (n = 2313) addressed positive impacts. The negative comments had a mean of 24.5 words per response (SD = 31.7, range: 1-560), while the positive comments had a mean of 21.3 words (SD = 27, range: 1-448). Five overarching themes characterized negative impacts of the virus in the lives of these older adults: disruption in daily life and plans; disruption in social relations; impact on health and wellness; healthcare and caregiving; and views on the pandemic. An additional five themes identified positive impacts: personal development; relationships; simpler life; benefits in work and finance; and introvert’s dream. Gender differences are consistent with expectations based on gender roles and activities: men were more likely to mention disrupted daily lives in particular as related to work, women were more likely to mention disrupted social relations, while health was mentioned to a comparable extent by both men and women. The negative themes illuminate the deep impact and disruption caused by the pandemic. The positive themes highlight adaptability and successful coping strategies which may be useful in the development of recovery plans and programming to help mitigate the negative effects of future 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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".