The quality of maternal-fetal and newborn care services in Jordan: a qualitative focus group study
Bibliographic record
Abstract
BACKGROUND: The antenatal, intrapartum, and postnatal periods are considered high-risk periods for the health of mothers and their newborns. Although the current utilization rate of some maternal and child care services in Jordan is encouraging, detailed information about the quality of these services is limited. Therefore, this study aimed to explore the quality of maternal-fetal and newborn antenatal care (ANC), delivery, and postnatal care (PNC) services in Jordan. METHODS: We conducted 12 focus group discussions (FGDs) with pregnant and postpartum women who attended maternal-child care services in three major hospitals in Jordan. All FGDs were recorded and transcribed verbatim. An inductive thematic analysis approach was used to identify themes and subthemes. RESULTS: The content analysis of the FGDs revealed a consensus among the discussants regarding the importance of ANC and PNC services for the health of mothers and their newborns. However, the participating women viewed ANC to be much more important than PNC. With regards to the choice between public and private antenatal care services, some of the discussants were disposed towards the private sector. Reasons for this included longer consultation time, a higher quality of services, better interpersonal and communication skills of healthcare providers, better treatment, more advanced equipment and devices, availability of female obstetricians, and more flexible appointment times. These women only perceived public hospital services to be necessary in cases of pregnancy-related complications and labor, as the costs of private sector services in such cases are too high. The findings also revealed that mothers usually only seek PNC services to check up on their newborn's health and not their own. CONCLUSION: Visiting private ANC clinics throughout pregnancy while giving birth in public facilities leads to the discontinuity and fragmentation in maternal-fetal and child healthcare services. To address this fragmentation, healthcare systems are proposed to establish interprofessional teamwork that requires different healthcare providers with complementary skills and practices in both public and private settings to work co-operatively and collectively. Investment in new technologies and interventions which enhance coordination and collaboration between public and private healthcare settings is necessary for the provision of non-traditional maternal healthcare.
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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".