Risk and Resilience Factors During the COVID-19 Pandemic: A Snapshot of the Experiences of Canadian Workers Early on in the Crisis
Bibliographic record
Abstract
Research highlights several risk and resilience factors at multiple ecological levels that influence individuals' mental health and wellbeing in their everyday lives and, more specifically, in disaster or outbreak situations. However, there is limited research on the role of these factors in the early days of the COVID-19 crisis. The present study examined if and how potential risk factors (i.e., reduction in income, job insecurity, feelings of vulnerability to contracting the virus, lack of confidence in avoiding COVID-19, compliance with preventative policies) and resilience factors (i.e., trait resilience, family functioning, social support, social participation, and trust in healthcare institutions) are associated with mental health and well-being outcomes, and whether these resilience factors buffer (i.e., moderate) the associations between risk factors and said outcomes. One to two weeks after the government recommended preventative measures, 1,122 Canadian workers completed an online questionnaire, including multiple wellbeing outcome scales in addition to measures of potential risk and resilience factors. Structural equation models were tested, highlighting that overall, the considered risk factors were associated with poorer wellbeing outcomes, except social distancing which was associated with lower levels of stress. Each of the potential resilience factors was found to have a main effect on one or more of the wellbeing outcomes. Moderation analysis indicated that in general these resilience factors did not, however, buffer the risk factors. The findings confirm that the COVID-19 crisis encompasses several stressors related to the virus as well as to its impact on one's social, occupational, and financial situation, which put people at risk for lower wellbeing as early as one to two weeks after the crisis began. While several resilience factors emerged as positively related to wellbeing, such factors may not be enough, or sufficiently activated at that time, to buffer the effects of the numerous life changes required by COVID-19. From an ecological perspective, while mental health professionals and public health decision-makers should offer/design services directly focused on mental health and wellbeing, it is important they go beyond celebrating individuals' inner potential for resilience, and also support individuals in activating their environmental resources during a pandemic.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".