A retrospective study of the impact of health worker strikes on maternal and child health care utilization in western Kenya
Bibliographic record
Abstract
BACKGROUND: There have been dozens of strikes by health workers in Kenya in the past decade, but there are few studies of their impact on maternal and child health services and outcomes. We conducted a retrospective survey study to assess the impact of nationwide strikes by health workers in 2017 on utilization of maternal and child health services in western Kenya. METHODS: We utilized a parent study to enroll women who were pregnant in 2017 when there were prolonged strikes by health workers ("strike group") and women who were pregnant in 2018 when there were no major strikes ("control group"). Trained research assistants administered a close-ended survey to retrospectively collect demographic and pregnancy-related health utilization and outcomes data. Data were collected between March and July 2019. The primary outcomes of interest were antenatal care (ANC) visits, delivery location, and early child immunizations. Generalized estimating equations were used to estimate risk ratios between the strike and control groups, adjusting for socioeconomic status, health insurance status, and clustering. Adjusted risk ratios (ARR) were calculated with 95% confidence intervals (95%CI). RESULTS: Of 1341 women recruited in the parent study in 2017 (strike group), we re-consented 843 women (63%) to participate. Of 924 women recruited in the control arm of the parent study in 2018 (control group), we re-consented 728 women (79%). Women in the strike group were 17% less likely to attend at least four ANC visits during their pregnancy (ARR 0.83, 95%CI 0.74, 0.94) and 16% less likely to deliver in a health facility (ARR 0.84, 95%CI 0.76, 0.92) compared to women in the control group. Whether a child received their first oral polio vaccine did not differ significantly between groups, but children of women in the strike group received their vaccine significantly longer after birth (13 days versus 7 days, p = 0.002). CONCLUSION: We found that women who were pregnant during nationwide strikes by health workers in 2017 were less likely to receive WHO-recommended maternal child health services. Strategies to maintain these services during strikes are urgently needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".