The lived experience of healthcare professionals working frontline during the 2003 SARS epidemic, 2009 H1N1 pandemic, 2012 MERS outbreak, and 2014 EVD epidemic: A qualitative systematic review
Bibliographic record
Abstract
To synthesize qualitative literature exploring the lived experience of healthcare workers (HCWs) who cared for patients during the following infectious disease outbreaks (IDOs): the 2003 SARS epidemic, 2009 H1N1 pandemic, 2012 MERS outbreak, and 2014 EVD epidemic. We aim to reveal the collective experience of HCWs during these four IDOs and to create a reference for comparison of current and future IDOs. Three electronic databases were searched, yielding 823 results after duplicates were removed. Forty qualitative and mixed-methods studies met the criteria for full file review. Fourteen studies met the inclusion and exclusion criteria. The data from the Results or Findings sections were manually coded and themes were conceptualized using thematic analysis. Of the 14 studies, 28.6% focused on SARS, 21.4% on H1N1, 21.4% on MERS, and 28.6% on EVD. Studies occurred in six different countries and included physicians, nurses, paramedics, and emergency medical technicians as participants. Five themes were conceptualized: Uncertainty, Adapting to Change, Commitment, Sacrifice, and Resilience. This review identified the collective experience of HCWs caring for patients during four 21st century IDOs. This qualitative systematic review offers a reference to compare similarities and differences of other IDOs, including the COVID-19 pandemic.
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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 | 0.036 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".