“If there are no female nurses to attend to me, I will just go and deliver at home”: a qualitative study in Garissa, Kenya
Bibliographic record
Abstract
BACKGROUND: The North Eastern region in Kenya experiences challenges in the utilization of maternal and newborn health services. In this region, culture and religion play a major role in influencing healthcare seeking behaviour of the community. This study was conducted to (i) understand key inherent barriers to health facility delivery in the Somali community of North Eastern Kenya and (ii) inform interventions on specific needs of this community. METHODS: The study was conducted among community members of Garissa sub-County as part of a baseline assessment before the implementation of an intervention package aimed at creating demand and increasing utilization of maternal and newborn services. Focus group discussions and key informant interviews were conducted with clan leaders, Imams, health managers, member of the county assembly, and service users (women and men) in three locations of Garissa sub-County. Data were analysed through content analysis, by coding recurrent themes and pre-established themes. RESULTS: Using health facility for delivery was widely acceptable and most respondents acknowledged the advantages and benefits of skilled birth delivery. However, a commonly cited barrier in using health facility delivery was the issue of male nurses and doctors attending to women in labour. According to participants, it is against their culture and thus a key disincentive to using maternity services. Living far from the health facility and lack of a proper and reliable means of transportation was also highlighted as a reason for home delivery. At the health facility level, respondents complained about the poor attitude of health care providers, especially female nurses being disrespectful; and the limited availability of healthcare workers, equipment and supplies. Lack of awareness and information on the importance of skilled birth attendance was also noted. CONCLUSION: To increase health facility delivery, interventions need to offer services that take into consideration the sociocultural aspect of the recipients. Culturally acceptable and sensitive services, and awareness on the benefits of skilled birth attendance among the community members are likely to attract more women to use maternity services and thus reduce adverse maternal and newborn health outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".