Perceptions and experiences of healthcare providers during COVID-19 pandemic in Karachi, Pakistan: an exploratory qualitative study
Bibliographic record
Abstract
OBJECTIVE: To explore healthcare provider's perspectives and experiences of the barriers and facilitators to treat and manage COVID-19 cases. DESIGN AND SETTING: We conducted an exploratory qualitative study using a purposive sampling approach, at a private tertiary care teaching hospital in Karachi, Pakistan. Study data were analysed manually using the conventional content analysis technique. PARTICIPANTS: Key-informant interviews (KIIs) were conducted with senior management and hospital leadership and in-depth interviews (IDIs) were conducted with front-line healthcare providers. RESULTS: A total of 31 interviews (KIIs=19; IDIs=12) were conducted, between April and May 2020. Three overarching themes emerged. The first was 'challenges faced by front-line healthcare providers working in COVID-19 wards. Healthcare workers experienced increased anxiety due to the fear of acquiring infection and transmitting it to their family members. They felt overwhelmed due to the exhaustive donning and doffing process, intense work and stigmatisation. The second theme was 'enablers supporting healthcare providers to deal with the COVID-19 pandemic'. Front liners pointed out several enabling factors that supported hospital staff including a safe hospital environment, adequate training, a strong system of information sharing and supportive management. The third theme was 'recommendations to support the healthcare workforce during the COVID-19 crisis'. Healthcare workers recommended measures to mitigate current challenges including providing risk allowance to front-line healthcare providers, preparing a backup health workforce, and establishing a platform to address the mental health needs of the healthcare providers. CONCLUSION: This study provides an initial evidence base of healthcare providers' experiences of managing patients with COVID-19 in the early stage of the pandemic and highlights measures needed to address the encountered challenges. It offers lessons for hospitals in low-income and middle-income countries to ensure a safe working environment for front-line workers in their fight against COVID-19.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".