‘It was hell in the community’: a qualitative study of maternal and child health care during health care worker strikes in Kenya
Bibliographic record
Abstract
BACKGROUND: Health care workers in Kenya have launched major strikes in the public health sector in the past decade but the impact of strikes on health systems is under-explored. We conducted a qualitative study to investigate maternal and child health care and services during nationwide strikes by health care workers in 2017 from the perspective of pregnant women, community health volunteers (CHVs), and health facility managers. METHODS: We conducted in-depth interviews and focus group discussions (FGDs) with three populations: women who were pregnant in 2017, CHVs, and health facility managers. Women who were pregnant in 2017 were part of a previous study. All participants were recruited using convenience sampling from a single County in western Kenya. Interviews and FGDs were conducted in English or Kiswahili using semi-structured guides that probed women's pregnancy experiences and maternal and child health services in 2017. Interviews and FGDs were audio-recorded, translated, and transcribed. Content analysis followed a thematic framework approach using deductive and inductive approaches. RESULTS: Forty-three women and 22 CHVs participated in 4 FGDs and 3 FGDs, respectively, and 8 health facility managers participated in interviews. CHVs and health facility managers were majority female (80%). Participants reported that strikes by health care workers significantly impacted the availability and quality of maternal and child health services in 2017 and had indirect economic effects due to households paying for services in the private sector. Participants felt it was the poor, particularly poor women, who were most affected since they were more likely to rely on public services, while CHVs highlighted their own poor working conditions in response to strikes by physicians and nurses. Strikes strained relationships and trust between communities and the health system that were identified as essential to maternal and child health care. CONCLUSION: We found that the impacts of strikes by health care workers in 2017 extended beyond negative health and economic effects and exacerbated fundamental inequities in the health system. While this study was conducted in one County, our findings suggest several potential avenues for strengthening maternal and child health care in Kenya that were highlighted by nationwide strikes in 2017.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".