Concerns and coping strategies of older adult Veterans in Canada at the outset of the COVID-19 pandemic
Bibliographic record
Abstract
Introduction The COVID-19 pandemic, including associated public health measures such as travel restrictions, cancellation of elective surgeries, and the closure of public spaces and retail services (full list available at: https://github.com/jajsmith/COVID-19NonPharmaceuticalInterventions ), has resulted in risks to the health and well-being of Veterans, including disruptions to healthcare, loss of income, social isolation, and viral infection and mortality. Although a few studies are ongoing to better understand who may be at greatest risk, little is known about how Veterans experienced the pandemic and what coping strategies they employed at the outset. This infographic summarizes national cross-sectional survey responses collected from 210 Veterans aged 55 years and older who participated in the Canadian COVID-19 Coping Study between May-June 2020 (Women’s College Hospital Research Ethics Board REB # 2020-0045-E). The average age of Veterans who participated was 72 years; 29% were female, 93% completed the survey in English and 84% were retired. This population is older and more likely to be female than the gen-eral Veteran population.4 None of the Veterans included in this study had been diagnosed with COVID-19 at the time of study. A total of 11% had a family member or friend with a diagnosis or symptoms, and less than 5% had a family member or friend hospitalized, or who died as a result of COVID-19.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".