Narratives on the frontline: A qualitative investigation of the lived experiences of healthcare workers during the COVID-19 pandemic in South Africa
Bibliographic record
Abstract
Background: During the COVID-19 pandemic, a mental health pandemic emerged. Frontline healthcare workers (HCWs) are arguably most affected, particularly in low-to-middle-income countries like South Africa. Understanding their experiences is needed to inform interventions for social and psychological support both now and for future pandemics. Aim: This study explored the lived experiences of frontline HCWs in South Africa during the COVID-19 pandemic using a lifeworld phenomenological framework.Methods: Semi-structured interviews were conducted and analysed using principles of reflexive thematic analysis.Setting: Our sample included 11 frontline HCWs from various professions and health sectors who worked with COVID-19 patients in South Africa. Results: HCWs’ lived experiences during the epidemic in South Africa were diverse and marked by contradictions. Work during COVID-19 was an emotional rollercoaster that was both mentally and emotionally exhausting, and the epidemic substantially impacted daily life. Limited psychological support and resources aggravated experiences. However, a positive narrative of hope and gratitude also resonated with participants.Conclusion: This study provides significant insights into the lived experiences of a diverse group of frontline South African HCWs during COVID-19. Qualitative methodologies provided depth and unique insights into the diverse realities of frontline HCWs.Contribution: This is one of the few studies in low-to-middle-income countries and the first in South Africa to use in-depth qualitative interviews to understand the lived experience of frontline HCWs. It demonstrates a shift in the definition of a ‘frontline’ HCW and highlights the need for greater psychological support and individualised public health interventions.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".