A cross-sectional survey on availability of facilities to healthcare workers in Pakistan during the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: COVID-19 pandemic has caused a healthcare crisis across the world. Low-economic countries like Pakistan lag behind in an adequate response including supply of Personal Protective Equipment (PPE), leading to panic among healthcare workers. We aim to evaluate hospital settings and state in Pakistan regarding availability of resources and views of healthcare workers on COVID-19. METHOD: A questionnaire survey was carried out among healthcare workers in public and private sector hospitals across Pakistan for a period of one month. The primary measured outcomes were presence of local Standard Operating Procedures (SOPs), availability and training of PPE, specific isolation wards and staff wellbeing support by the hospital management. RESULTS: There were 337 participants, 307 (91.1%) doctors and 11 nurses (3.3%). About two-third of the participants (n = 199, 59%) reported non-availability of PPE and 40% (n = 136) denied availability of local Standard Operating Procedures. About a quarter of the participants (n = 94, 27.8%) had training in Donning and Doffing. Most of the participants (n = 277, 82.1%) felt that it was necessary to have testing available for frontline workers. CONCLUSION: There is lack of PPE and adequate facilities in hospitals as COVID-19 continue to spread in Pakistan. Local medical governing bodies and societies should come forward with guidelines to ascertain wellbeing of the healthcare workers.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".