Implementation of a coronavirus disease 2019 infection prevention and control training program in a low-middle income country
Bibliographic record
Abstract
INTRODUCTION AND AIMS: The COVID-19 pandemic poses an ongoing risk to health workers globally. This is particularly true in low- and middle-income countries (LMICs) where resource constraints, ongoing waves of infection, and limited access to vaccines disproportionately burden health systems. Thus, infection prevention and control (IPC) training for COVID-19 remains an important tool to safeguard health workers. We report on the implementation of evidence-based and role-specific COVID-19 IPC training for health workers in a hospital and public health field setting in Sri Lanka. METHODS: We describe the development of training materials, which were contextualized to local needs and targeted to different staffing categories including support staff. We describe development of role- and context-specific IPC guidelines and accompanying training materials and videos during the first year of the COVID-19 pandemic. We describe in-person training activities and an overview of session leadership and participation. RESULTS: Key to program implementation was the role of champions in facilitating the training, as well as delivery of training sessions featuring multi-media videos and role play to enhance the training experience. A total of 296 health workers participated in the training program sessions. Of these, 198 were hospital staff and 98 were from the public health workforce. Of the 296 health workers who participated in a training session, 277 completed a pre-test questionnaire and 256 completed post-test questionnaires. A significant increase in knowledge score was observed among all categories of staff who participated in training;however, support staff had the lowest pre-test knowledge on IPC practices at 71%, which improved to only 77% after the formal class. CONCLUSION: Implementing an IPC training program during a complex health emergency is a challenging, yet necessary task. Leveraging champions, offering training through multiple modalities including the use of videos and role play, as well as inclusion of all staff categories, is crucial to making training accessible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".