Exploring Religious leaders’ experiences and challenges on Childbirth at Health Institutions. A qualitative study
Bibliographic record
Abstract
Abstract Background Childbirth at health institutions is critical to preventing major maternal and newborn deaths. In low and middle-income countries, many women still give childbirth without skilled assistance. Religious leaders may play a crucial role to promote childbirth at health institutions. So, this study aims to explore religious leaders’ experiences and challenges in childbirth preparedness and childbirth at health institutions. Methods After ethical approval was secured from Jimma University, Ethiopia, and the University of Ottawa, Health Sciences and Research Ethics Boards, Canada an exploratory study was conducted from Nov 2016 to February 2017. Data were collected from 24 religious leaders. Atlas ti software 7.5.18 package was used to assist the analysis. Identified themes and categories were interpreted and discussed with related studies. Results Lower awareness level, family needs for traditional birth rituals at home, lack of access to roads and transportation, lack of medical supplies, poor quality of health care provision and lack of respect for laboring mothers were the challenges raised by study participants. There was a traditional way of childbirth preparedness but is not matched due to economic status and level of awareness. The majority are inclined to say that destiny of maternal health outcome is determined by God/Allah’s will though not contradicting childbirth at a health institution. Conclusion A comprehensive approach to include religious leaders to increase awareness and positive beliefs towards childbirth at health institutions should be considered. Health institution factors such as respect for laboring mothers, medical supplies, and equipment should be improved. Access to roads or transportation also needs to be communicated to responsible bodies and community leaders to improve transportation problems.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".