Implementation challenges of maternal health care in Ghana: the case of health care providers in the Tamale Metropolis
Bibliographic record
Abstract
BACKGROUND: Achieving the Millennium Development Goal (MDG) of improving maternal health has become a focus in recent times for the majority of countries in sub-Saharan Africa. Ghana's maternal mortality is still high indicating that there are challenges in the provision of quality maternal health care at the facility level. This study examined the implementation challenges of maternal health care services in the Tamale Metropolis of Ghana. METHODS: Purposive sampling was used to select study participants and qualitative strategies, including in-depth interviews, focus group discussions and review of documents employed for data collection. The study participants included midwives (24) and health managers (4) at the facility level. RESULTS: The study revealed inadequate in-service training, limited knowledge of health policies by midwives, increased workload, risks of infection, low motivation, inadequate labour wards, problems with transportation, and difficulties in following the procurement act, among others as some of the challenges confronting the successful implementation of the MDGs targeting maternal and child health in the Tamale Metropolis. CONCLUSIONS: Implementation of maternal health interventions should take into consideration the environment or the context under which the interventions are implemented by health care providers to ensure they are successful. The study recommends involving midwives in the health policy development process to secure their support and commitment towards successful implementation of maternal health interventions.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".