Mental Health and Well-Being Needs among Non-Health Essential Workers during Recent Epidemics and Pandemics
Bibliographic record
Abstract
Essential workers, those who work in a variety of sectors that are critical to sustain the societal infrastructure, were affected both physically and mentally by the COVID-19 pandemic. While the most studied group of this population were healthcare workers, other essential non-health workers such as those working in the law enforcement sector, grocery services, food services, delivery services, and other sectors were studied less commonly. We explored both the academic (using MEDLINE, PsycInfo, CINAHL, Sociological Abstracts, and Web of Science databases) and grey literature (using Google Scholar) to identify studies on the mental health effects of the six pandemics in the last 20 years (2000-2020). We identified a total of 32 articles; all of them pertained to COVID-19 except for one about Ebola. We found there was an increase in depression, anxiety, stress, and other mental health issues among non-health essential workers. They were more worried about passing the infection on to their loved ones and often did not have adequate training, supply of personal protective equipment, and support to cope with the effects. Generally, women, people having lower education, and younger people were more likely to be affected by a pandemic. Exploring occupation-specific coping strategies of those whose mental health was affected during a pandemic using more robust methodologies such as longitudinal studies and in-depth qualitative exploration would help facilitate appropriate responses for their recovery.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".