Support for health care workers and psychological distress: thinking about now and beyond the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: This study explores the relationship between emotional support, perceived risk and mental health outcomes among health care workers, who face high rates of burnout and mental distress since the beginning of the COVID-19 pandemic. METHODS: A cross-sectional, multicentred online survey of health care workers in the Greater Toronto Area, Ontario, Canada, during the first wave of the COVID-19 pandemic evaluated coping strategies, confidence in infection control, impact of previous work during the 2003 SARS outbreak and emotional support. Mental health outcomes were assessed using the Generalized Anxiety Disorder scale, the Impact of Event Scale - Revised and the Patient Health Questionnaire (PHQ-9). RESULTS: Of 3852 participants, 8.2% sought professional mental health services while 77.3% received emotional support from family, 74.0% from friends and 70.3% from colleagues. Those who felt unsupported in their work had higher odds ratios of experiencing moderate and severe symptoms of anxiety (odds ratio [OR] = 2.23; 95% confidence interval [CI]: 1.84-2.69), PTSD (OR = 1.88; 95% CI: 1.58-2.25) and depression (OR = 1.88; 95% CI: 1.57-2.25). Nearly 40% were afraid of telling family about the risks they were exposed to at work. Those who were able to share this information demonstrated lower risk of anxiety (OR = 0.58; 95% CI: 0.48-0.69), PTSD (OR = 0.48; 95% CI: 0.41-0.56) and depression (OR = 0.55; 95% CI: 0.47-0.65). CONCLUSION: Informal sources of support, including family, friends and colleagues, play an important role in mitigating distress and should be encouraged and utilized more by health care workers.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".