Delivering Health Services during Early Days of COVID-19 Pandemic: Perspectives of Frontline Healthcare Workers in Kenya’s Urban Informal Settlements
Bibliographic record
Abstract
The COVID-19 pandemic has caused widespread disruptions to health, economic and social lives globally. This qualitative study explores frontline healthcare workers’ (HCWs) experiences delivering routine care in Kenya’s informal settlements during the early phases of the pandemic, amidst stringent COVID-19 mitigation measures. Thirteen telephone interviews were conducted with facility and community-based HCWs serving three informal settlements in Nairobi and Mombasa. Data were analyzed using the framework approach. Results indicate there were widespread fears and anxieties surrounding COVID-19 and its management. Secondly, access to facility-based care at the onset of the pandemic was reported to decline, with service availability hampered by the imposed curfew hours and guidance on the maximum allowable number of clients. HCWs experienced heightened risk of COVID-19 infection due to poor working conditions including inadequate personal protective equipment (PPE) and unavailable isolation areas for COVID-19 positive patients. HCWs also experienced stigma associated with contact with persons suspected of having COVID-19 infection, thereby causing a strain on their mental health and wellbeing. The study recommends the need for interventions to support and protect HCWs’ physical and mental health, alongside health system preparedness. Additionally, it is vital to identify ways of taking health services closer to the community to address access barriers in health emergency contexts.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".