A Comparative Cross-Sectional Psychological analysis of psychosocial and mental health issues faced by frontline healthcare professionals during COVID- 19 pandemic across various countries
Bibliographic record
Abstract
Background: COVID-19 was declared as global pandemic by WHO by March 2020.Since then, overwhelming workload, inadequate human resources, technology, personal protective gear and workplace harassment, could cause stress, anxiety or depression among the healthcare professionals.Being care givers to the society it was imperative to evaluate and assess the impact of the pandemic to find a potential ground to make adequate amendments to ensure good mental health of our professionals.Aim: The present study was designed with the objectives to evaluate and compare levels stress, anxiety and depression in healthcare professionals working in India and countries other than India.Setting and Design: This was a cross-sectional study conducted among 200 participants (100 Indians and 100 from other countries-USA, Canada.Method: A questionnaire link through Google form was distributed among healthcare professionals after taking consent.The study was approved by the institutional ethical committee. Statistical Analysis:The results of the two groups were compared using chi square test to observe a difference of significance among them.Result and Conclusion: On analysis of questionnaire regarding mental health of health care professionals, Severe stress and anxiety were significantly higher among Indian female HCPs (17% and 50% respectively) compared to other countries (6% and 22% respectively) while borderline stress (69%), anxiety (39%) and depression(26%) was more prevalent among healthcare professionals of other countries.Media projection about workplace violence and workplace job security needs to be taken well care of to protect the mental health of HCPS in India.
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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 | 0.002 |
| 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) | 0.002 | 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".