Exploring the Ethical Tensions Experienced by Health Care Workers during Infectious Disease Outbreaks in Low- and Middle-income Countries: A Critical Interpretive Review of the Literature
Bibliographic record
Abstract
OBJECTIVE: This review aimed to map the main ethical tensions experienced by health workers in low- and middle-income countries during infectious disease outbreaks. METHODS: We conducted a critical interpretive review of qualitative research studies. After searching 3 databases, 4445 articles were exported to Rayyan, deduplicated, and screened for eligibility. Of the 98 articles retained for full review, 25 met the inclusion criteria. Data were extracted to an Excel spreadsheet and key ethical tensions were identified using a descriptive content and thematic analysis approach. RESULTS: Twenty-three of the studies focused on the COVID-19 pandemic, and two addressed Ebola epidemics. Three major ethical tensions were experienced by health workers, which involved conflicts between their professional duty to patients, colleagues, and communities, as against their concerns for personal safety, the well-being of their families, and facing stigma and discrimination. Secondary tensions arose when health workers seeking to manage these primary ethical tensions experienced further uncertainty about whether to disclose information about their professional roles with family members or community. CONCLUSIONS: Ethical tensions are unavoidable during contagions, and may be amplified due to structural features. Authorities must take steps to support health workers as they navigate ethical tensions during localized epidemics or global pandemics.
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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 | 0.106 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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 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".