Factors Associated with Older People’s Anxiety Symptom Positioning after COVID-19: Cross-Sectional Findings from a Canadian Sample
Bibliographic record
Abstract
This study sheds light on the personal characteristics of older Canadians self-identifying as severely anxious and the coping strategies that they gravitated to mitigate their anxiety. Our studied sample consisted of 606 Canadians aged 60 and above who took part in an e-survey across all 10 of Canada’s provinces, launched in July 2022, when social distancing was lifted across the country. Participants completed a personal characteristics questionnaire, the Geriatric Anxiety Scale or GAS-10, and a checklist of everyday coping strategies for mitigating anxiety. A seemingly greater number of severely anxious Canadians were born female, self-identified as a cisgender woman, and were in their 60s and in poor to fair health. A univariate logistic regression analysis revealed that all such personal characteristics were associated with statistically significantly greater odds of experiencing severe anxiety. In our multivariate logistic regression analysis, no significant differences were observed between the sexes (AOR = 0.590, p = 0.404), and non-binary and cisgender men (AOR = 0.689, p = 0.441) and women (AOR = 0.657, p = 0.397). Nor were there statistically significant differences in the odds of experiencing severe anxiety for those living with versus without a life partner and chronic illnesses. Older Canadians experiencing severe anxiety were far more likely to normalize their fear and anxiety (AOR = 4.76, p < 0.001), challenge their worries (AOR = 5.21, p < 0.001), and to relax or meditate (AOR = 2.36, p = < 0.001). They were less inclined to decrease other sources of stress in their lives, to stay active, and to get enough sleep. We offer anticipatory guidance for mental health program planners and practitioners, and fruitful avenues of inquiry for researchers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".