A longitudinal study of the COVID-19 pandemic impact on mental health in ophthalmic personnel and students
Bibliographic record
Abstract
BACKGROUND: Our previous study revealed that the COVID-19 pandemic posed mental health challenges to eye care professionals and students. The intent of this study was to identify the longitudinal impact of the COVID-19 pandemic on mental health among ophthalmic personnel and students. Additionally, the potential risk factors for mental health problems were investigated. METHODS: A two-phase survey among eye care professionals and students in the USA and Canada was conducted. Phase 1 was administrated from June 23 to July 8, 2020, and has been published; Phase 2 was conducted from January 21 to February 2, 2021. A total of 824 eye care professionals and students participated in Phase 2, with a response rate of 44.1%. Symptoms of depression, anxiety, and stress were measured and calculated. RESULTS: Compared with Phase 1, stress scores in Phase 2 were significantly reduced (P<0.001) although they were still higher than the pre-COVID level (P < .001). Scores for depression (P = 0.20) and anxiety (P = 0.40) showed no change, comparing Phase 2 to Phase 1. Reduction of stress scores significantly differed among occupations (P = 0.005); students had less reduction of stress than other eye care professionals (all P<0.05). Vaccination status was significantly associated with a reduction in stress scores (P = 0.04). CONCLUSIONS: With the continuing COVID pandemic, although stress level was reduced at 7-month follow-up, the mental health of eye care professionals was still impacted. In this population, COVID-19 vaccination was associated with fewer stress symptoms. These results indicate that the COVID-19 pandemic continues impacting mental health among eye care professionals, especially students. These study results warrant future interventions for eye care personnel and students to prevent or treat mental health disorders.
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 | 0.003 |
| 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 | 0.001 |
| 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".