Enhancing focused antenatal care in Ghana: An exploration into perceptions of practicing midwives
Bibliographic record
Abstract
Objective The specific objectives of this study were to explore the perceptions of midwives on focused antenatal care at a large urban hospital in Tema, Ghana. Methodology An interpretive descriptive design was used to explore, interpret and describe the perceptions of midwives in the provision of focused antenatal services to pregnant women. Purposive sampling techniques were used to recruit participants (midwives). Data were collected by conducting individual semi-structured interviews. The recorded interviews were transcribed verbatim. Data were manually coded using two methods described by Saldana (2009). Guba’s model of trustworthiness was implemented. Findings Five themes emerged from the data analysis. It included midwives’ conceptualization of FANC and their perception of FANC processes/flow, quality of care, factor inhibiting the implementation of FANC, and strategies to enhance FANC interventions. Discussion Continuous quality management is essential to ensure a supportive environment to deliver FANC services. Continued and increased support from Ghana Health Service (GHS) will be of great importance. Conclusion It is clear that the midwives in this study perceived FANC positive. FANC contributes to the quality of ANC delivery and subsequent improvement in the health status of pregnant women in Ghana. In addition, the findings contributed to existing knowledge and have the potential to guide future research in the field of ANC to improve maternal health and reduce maternal deaths .
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".