Exploring healthcare providers’ perceptions of mental health amid COVID-19 pandemic in obstetrics and gynaecology department of a tertiary care public sector hospital of Karachi, Pakistan: an exploratory qualitative study protocol
Bibliographic record
Abstract
INTRODUCTION: In the wake of the unprecedented public health challenge of the COVID-19 pandemic, it is highly significant to recognise the mental health impact of this mounting threat on healthcare providers (HCPs) working in the obstetrics and gynaecology department. Experience from epidemics and emerging literature around COVID-19 show that the unparalleled amount of stress that HCPs are dealing with is linked with the increased burden of mental health conditions. We aim to conduct an exploratory qualitative descriptive study to assess HCPs' perceptions of mental health amid the COVID-19 pandemic in the obstetrics and gynaecology department of a public sector tertiary care hospital of Karachi, Pakistan. METHODS AND ANALYSIS: This study will use a qualitative descriptive approach where approximately 20-25 HCPs from the obstetrics and gynaecology department will be recruited using a purposive sampling approach. Data will be collected through semistructured interviews and it will be analysed thematically using NVivo V.12 Plus software. ETHICS AND DISSEMINATION: Ethical approval for this study has been obtained from the Institutional Review Board Committee of Jinnah Postgraduate Medical Center hospital. The study results will be disseminated to the scientific community and the HCPs participating in the study. The findings will help us to explore the doctor's perceptions of mental health during the current pandemic of COVID-19 and its impact on their daily lives and mental well-being.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".