Employee Mental Health During COVID-19 Adaptation: Observations of Occupational Safety and Health/Human Resource Professionals in Ireland
Bibliographic record
Abstract
Objectives: This study aims to understand mental health issues among Irish employees arising from COVID-19 adaptation from the perspective of Occupational Safety and Health (OSH) and/or Human Resource (HR) professionals. Methods: Fifteen focus groups including 60 OSH/HR professionals from various sectors were conducted covering four predetermined themes. The data were transcribed verbatim, with transcripts entered into Nvivo for thematic analysis incorporating intercoder reliability testing. Results: The mental health impacts among employees are identified from three stages: pre-adaptation, during adaptation, and post-adaptation. Most issues were reported during the second stage when working conditions dramatically changed to follow emerging COVID-19 policies. The identified mental health support from participating organizations included providing timely and reliable information, Employee Assistance Programme (EAP), informal communication channels, hybrid work schedules and reinforcement of control measures. Conclusion: This study explores the challenges facing employees during the different stages of COVID-19 adaptation and the associated mental health impacts. Gender’s influence on mental health consultations should be considered when planning for public health emergencies, and further research conducted in male dominated industries.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".