Educational needs for infection prevention and control during outbreaks: A qualitative study with health workers in Sri Lanka
Bibliographic record
Abstract
BACKGROUND: Protecting the health workforce is essential to health systems resilience for emerging infectious disease (EID) outbreaks. We explored healthcare workers (HCWs) perceptions of infection prevention and control (IPC) guidelines and training needs for managing the Coronavirus Disease 2019 (COVID-19) pandemic in Sri Lanka as part of a larger study, which aimed to create role specific IPC guidelines for HCWs in low-and-middle-income countries (LMICs). METHODS: Using a qualitative descriptive approach, sixteen semi-structured interviews were conducted among hospital and public health HCWs including, physicians, nurses, public health midwives and support staff, such as cleaning staff, in Kalutara District of Sri Lanka. RESULTS: Interview findings are described under three themes: HCW workload during an EID outbreak; evolving EID management guidance and education during a public health emergency; and desired EID guidance and IPC education during a public health emergency. The COVID-19 pandemic increased staff workload across the spectrum; HCWs were provided with some form of IPC training but there were lapses in adherence; and staff were interested in having easy to use desk guides, training videos, formal training and access to all training material. CONCLUSION: A tailored approach to IPC education based on identified overall and key specific needs (such as training support staff) provides crucial information to improve HCW knowledge of IPC practices in Sri Lanka. In addition, IPC education must be extended to all HCWs to sustain best practices before, during, and after health emergencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".