Coping during the COVID-19 pandemic: A mixed methods approach to understand how social factors influence coping ability
Bibliographic record
Abstract
The COVID-19 pandemic and public health protection measures aimed at mitigating the transmission of the virus have both resulted in tremendous physical and mental health impacts. The study at hand used a gender-based analysis and social determinants of health approach to investigate which communities had trouble coping during times of strict protection measures and symptoms and strategies employed during the COVID-19 pandemic. Participants were recruited from previously established cohorts as a part of the COVID-19 Rapid Evidence Study of a Provincial Population-Based Cohort for Gender and Sex (RESPPONSE) study. Being a young adult, female, woman, gender diverse, low-income earner or LGBTQ/2S+ was significantly associated with not being able to cope during the first wave of the pandemic. The effects for females, women, and gender diverse were attenuated yet still significant when controlling for various covariates. Those who reported not coping were more likely to present maladaptive coping symptoms and strategies. Our findings demonstrate the need to support marginalized communities in coping with the current ongoing COVID-19 pandemic and build proactive support for 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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".