Exploring the Factors Influencing Home Delivery: A Cross Sectional Study among Rural Residents in Tamale Metropolis
Bibliographic record
Abstract
Introduction: Maternal and child mortality continues as a disturbing public health problem facing Ghana and other low and middle-income countries. As such there are advocacy for skilled birth delivery. Despite the advocacy, in most rural communities, unskilled delivery still persists. It is against this background that this study aims to explore the factors influencing home delivery among women in rural areas of Tamale Metropolis.
 Methods: The study employed the descriptive cross-sectional design with the mixed method approach. The study recruited 200 women within reproductive age of 18-49 who had delivered at least once in the selected rural communities in the Tamale Metropolis. Questionnaire and interview guide were used to collect data for this study. The data was analyzed using SPSS version 26 and manual thematic content analysis for quantitative and qualitative data respectively.
 Results: The prevalence of home delivery was reported as (36.5%) among the respondents. More than quarter (35.5%) of the respondents rate their satisfaction of home delivery as good whereas more than half of the respondents rated TBAs’ attitude as good. Three (3) qualitative respondents indicated that most women chose to deliver at home in neglect of hospital because of better satisfaction gained from delivering at home. The qualitative data showed that the factors identified to influence home delivery were as followed: negative attitude of health workers, lack of trust in health workers competence, unnecessary expenses from hospitals, distance, and poor quality of services. Nearly 90.0% of the respondents were enrolled on the NHIS with a proof of active NHIS card. Majority of the respondents were aware of the free maternal health services and all respondents agreed that they had a CHPs compound in their communities, however, only 36.5% responded in the affirmative that these CHPs compound are resourced adequately.
 Conclusion: The study showed a substantive percentage of the respondents to be practicing home delivery with the support of unskilled birth attendants. Poor attitudes from health workers, better previous experience in delivering at home, unreliable means of transport to the hospital and additional informal delivery expenses were some of the factors found to be significant determinants of home delivery. It is recommended for the government and other stakeholders to provide rural ambulance services to aid in the transport of pregnant women in labor to the nearest health facility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".