Exploring COVID‐19 from the perspectives of healthcare personnel in Malawi
Bibliographic record
Abstract
Background: The Coronavirus 2019 disease (COVID-19) brought many healthcare systems around the world to the point of collapse all the while putting the lives of healthcare workers at risk. This study forgoes an institutional look at healthcare to center individual healthcare personnel in Malawi to better understand (1) how the worldviews of healthcare workers impact their work in the context of COVID-19, (2) how COVID-19 impacted healthcare workers, and (3) the unique conditions faced by being a healthcare worker in a low-income nation. Methods: = 15) with healthcare workers, traditional healers, and hospital leadership. The data collected were inductively coded and analyzed using the framework method, producing rich descriptions on how COVID-19 impacted the lifeworlds of healthcare workers in Malawi. Results: The findings reveal many of the struggles healthcare workers faced due to misaligned government policy and perceived proximity to COVID-19; outline their needs such as wanting better resources, funds, wages, and public health communication; and, exemplify the significant role that personal biases, worldviews, and sense of fear played in how healthcare workers perceived and interacted with COVID-19. Conclusion: Much of what was said echoes beyond borders, reflecting common global sentiments felt by healthcare personnel, and offers directions to explore building policies, strategies, and plans in preparation for any future disease outbreaks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".