Evaluation of a piloted digital reproductive health registry in Jordan to improve mother and child health
Bibliographic record
Abstract
BACKGROUND: Primary data on antenatal care services in Jordan are stored in diverse systems among hospitals and Mother & Child Health (MCH) centers. The resulting information flow gaps between healthcare levels challenge the quality and continuity of MCH care. To close these data and care delivery gaps, a harmonized Reproductive Health Registry (hRHR) was designed and piloted to bring data in a consistent/digital/readily accessible format and enhance the current health information system. Our study evaluated the perceptions on the newly developed hRHR's improvement of the delivery of sexual and reproductive health (SRH) services in 19 healthcare centers where the hRHR was piloted. METHODS: We utilized a mixed methodology (qualitative and quantitative assessments). Three tools were used for Key Informants/stakeholders, services providers, and women. RESULTS: A total of 13 SRH stakeholders, 37 service providers, and 855 women/service users participated in this evaluation. All SRH stakeholders agreed that the hRHR is responding to a need for digitalization of routine MCH data, medical files, and reports. They all agreed that the synchronization with the currently used Computerized Patient Record System (CPRS) and the hRHR Web-based application will enhance the expansion and needed upgrades in the future. For service providers, 73% were satisfied with the system, 78.4% were willing to keep using it, and 81.1% indicated that the hRHR is highly useful to health providers. For women/service users, 89.2% agreed/strongly agreed that the new system improves the confidentiality and privacy of their health information, and 89.7% agreed/strongly agreed that the new system allows health staff to easily access patient information. There were also foreseeable challenges including fragmentation of electronic data management, lack of connectivity across health services, limited ownership, dependence on external funding as impeding factor for scalability, poor IT infrastructure still disrupting service in MCH centers and affecting data entry, and limited electronic connectivity/reporting access impacting the continuity of care. CONCLUSION: The new hRHR has a high acceptance level among stakeholders, health providers and women using MCH services. The evaluation showed that the system improved documentation of data, decreased time, and effort of data reporting and retrieval, and improved access to patient data.
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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 | 0.032 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".