Risk factors for mental health symptoms during the COVID-19 pandemic in ophthalmic personnel and students in USA (& Canada): a cross-sectional survey study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic poses mental health challenges to frontline healthcare workers. Eye care professionals may be especially susceptible to mental health problems due to high-risk exposures to patients. Yet, no prior research has studied mental health issues among eye care professionals during the COVID-19 pandemic. OBJECTIVE: The purpose of this study was to identify risk factors for mental health problems during the COVID-19 pandemic among eye care professionals. METHODS: We conducted a cross-sectional survey study among eye care professionals and students in the United States and Canada from June 23 to July 8, 2020 during the COVID-19 pandemic. A total of 8505 eye care professionals and students received email invitations to the survey and 2134 participated. We measured mental health outcomes including symptoms of depression, anxiety, and stress using validated scales, as well as potential risk factors including demographic characteristics, state-level COVID-19 case counts, participants' patient interactions, childcare responsibilities, and pre-pandemic stress levels. Linear multiple regression and logistic regression analyses were used to determine relationships between risk factors and mental health outcomes. RESULTS: We found that 38.4% of eyecare professional participants in the survey met screening threshold as probable cases of anxiety, depression, or both during the COVID-19 pandemic. Controlling for self-reported pre-pandemic stress level and state COVID-19 case daily cases, significant risk factors for depression, anxiety, and psychological stress during the COVID-19 pandemic included: being female, younger age, and being Black or Asian. Interestingly, we found two somewhat surprising protective factors against depression symptoms: more frequent interactions with patients and having a greater proportion of childcare responsibilities at home. CONCLUSIONS: This study showed a high prevalence of mental health problems and revealed disparities in mental health among eye care personnel and students: Female, younger, Black, and Asian populations are particularly vulnerable to mental health issues. These results indicate that it is critical to identify mental health issues more effectively and develop interventions among this population to address this significant and growing public health issue. The strategies and policies should be reflective of the demographic disparities in this vulnerable population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".