O-274 HOW DID REMOTE E-WORKING CONDITIONS AFFECT THE MENTAL HEALTH OF HEALTHCARE EMPLOYEES WHO WORKED FROM HOME OR PERFORMED VIRTUAL WORK DURING THE COVID-19 PANDEMIC?
Bibliographic record
Abstract
Abstract Introduction Use of remote work remains elevated post COVID-19. Understanding how remote working conditions influence mental health can inform appropriately tailored interventions. The objective was to examine the association between remote working conditions and mental health among workers employed in healthcare during the COVID-19 pandemic in Canada. Methods Cross-sectional survey of employees who worked remotely for a regional health authority. Ordinal logistic models adjusted for potential confounders examined the relationship between remote working conditions, measured via the E-Work Life Scale, and mental health outcomes. Results The final analytic sample included 399 respondents. In adjusted models, greater work-life balance while remote working was associated with decreased levels of: anxiety (OR = 0.52, CI: 0.38, 0.71), burnout (OR = 0.27, 95% CI: 0.19, 0.38), and poor self-rated mental health (OR = 0.45, 95% CI: 0.33, 0.62); but not depression. Greater organizational trust was associated with decreased: depression (OR 0.53, 95% CI: 0.37, 0.77), anxiety (OR 0.57, 95% CI: 0.42, 0.79), and poor self-rated mental health (OR 0.53, 95% CI: 0.39, 0.71); but not burnout. Greater flexibility was associated with decreased burnout (OR 0.62, 95% CI: 0.45, 0.85), but not the other mental health outcomes. Productivity was not significantly associated with mental health outcomes. Discussion The findings support a strong association between remote working conditions, particularly, work-life balance and organizational trust, and mental health outcomes. Conclusion Remote working conditions associated with work-life balance and organizational trust may serve as a starting point for the monitoring of remote working conditions and development of mental health supports.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".