In-person training on COVID-19 case management and infection prevention and control: Evaluation of healthcare professionals in Bangladesh
Bibliographic record
Abstract
BACKGROUND: As COVID-19 was declared a global pandemic, the major focus of healthcare organizations shifted towards preparing healthcare systems to handle the inevitable COVID-19 burden at different phases and levels. A series of in-person training programs were operated in collaboration with government and partner organizations for the healthcare workers (HCW) of Bangladesh. This study aimed to assess the knowledge of HCWs regarding SARS-CoV-2 infection, their case management, infection prevention and control to fight against the ongoing pandemic. METHODS: As a part of the National Preparedness and Response Plan for COVID-19 in Bangladesh, the training program was conducted at four district-level hospitals and one specialized hospital in Bangladesh from July 1, 2020 to June 30, 2021. A total of 755 HCWs participated in the training sessions. Among them, 357 (47%) were enrolled for the evaluation upon completion of the data, collected from one district hospital (Feni) and one specialized hospital (National Institute of Mental Health). RESULTS: The mean percentage of pre-test and post-test scores of all the participants were found to be 57% (95% CI 8.34-8.91; p 0.01) and 65% (95% CI 9.56-10.15; p <0.001) respectively. The difference of score (mean) between the groups was significant (p<0.001). After categorizing participants' knowledge levels as poor, average and fair, doctors' group has shown to have significant enhancement from level of average to fair compared to that of the nurses. Factors associated with knowledge augmentation of doctors were working in primary health care centers (aOR: 4.22; 95% CI: 1.80, 9.88), job experience less than 5 years (aOR: 4.10; 95% CI: 1.01, 16.63) and experience in caring of family member with COVID-19 morbidity (aOR: 2.06; 95% CI: 1.03, 4.10), after adjusting for relevant covariates such as age, sex and prior COVID-19 illness. CONCLUSION: Considering the series of waves of COVID-19 pandemic with newer variants, the present paper underscores the importance of implementing the structured in-person training program on case management, infection prevention and control for the HCWs that may help for successful readiness prior to future pandemics that may further help to minimize the pandemic related fatal consequences.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".