The impact of supply-side and demand-side interventions on use of antenatal and maternal services in western Kenya: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Antenatal care (ANC) and delivery by skilled providers have been well recognized as effective strategies to prevent maternal and neonatal mortality. ANC and delivery services at health facilities, however, have been underutilized in Kenya. One potential strategy to increase the demand for ANC services is to provide health interventions as incentives for pregnant women. In 2013, an integrated ANC program was implemented in western Kenya to promote ANC visits by addressing both supply- and demand-side factors. Supply-side interventions included nurse training and supplies for obstetric emergencies and neonatal resuscitation. Demand-side interventions included SMS text messages with appointment reminders and educational contents, group education sessions, and vouchers to purchase health products. METHODS: To explore pregnant mothers' experiences with the intervention, ANC visits, and delivery, we conducted focus group discussions (FGDs) at pre- and post-intervention. A total of 19 FGDs were held with pregnant mothers, nurses, and community health workers (CHWs) during the two assessment periods. We performed thematic analyses to highlight study participants' perceptions and experiences. RESULTS: FGD data revealed that pregnant women perceived the risks of home-based delivery, recognized the benefits of facility-based delivery, and were motivated by the incentives to seek care despite barriers to care that included poverty, lack of transport, and poor treatment by nurses. Nurses also perceived the value of incentives to attract women to care but described obstacles to providing health care such as overwork, low pay, inadequate supplies and equipment, and insufficient staff. CHWs identified the utility and limitations of text messages for health education. CONCLUSIONS: Future interventions should ensure that adequate workforce, training, and supplies are in place to respond to increased demand for maternal and child health services stimulated by incentive programs.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".