The Jordan Stillbirth and Neonatal Mortality Surveillance (JSANDS) System: Evaluation Study
Bibliographic record
Abstract
BACKGROUND: The Jordan Stillbirth and Neonatal Mortality Surveillance (JSANDS) is an electronic surveillance system that automatically transfers the data on births, stillbirths, and neonatal deaths to the concerned authorities in the Ministry of Health. JSANDS was implemented and tested in 5 maternity hospitals during the period spanning May 2019 through December 2020. OBJECTIVE: This study aimed to evaluate the usefulness and performance of JSANDS to register births, stillbirths, and neonatal deaths, and determine their causes. Specifically, this study examined the JSANDS attributes of acceptability, simplicity, flexibility, stability, representativeness, sustainability, penetration, data quality, sensitivity, and adoption. METHODS: An evaluation study was conducted after 18 months of the JSANDS implementation using the Updated Guidelines for Evaluating Public Health Surveillance Systems. The evaluation focused on how well the system operated to meet its purpose and objectives. The indicators assessing the system attributes were scored on a Likert scale. Each indicator and overall attribute percentage score was represented as score rank and interpreted as excellent (score ≥80%), good (score ≥60 and <80%), average (score ≥40 and <60%), and poor (score <40%). RESULTS: A total of 270 health care professionals participated in this study and evaluated the system performance. The system users rated the usefulness of JSANDS as excellent (percentage score=85.6%). The overall acceptability (percentage score=82.3%), flexibility (percentage score=80.2%), stability (percentage score=80.0%), and representativeness (percentage score=86.6%) were also rated excellent. The overall simplicity was scored good (percentage score=75.4%). All participants were trained on JSANDS and used it in the past 12 months. Of the 270 respondents, 219 (86.2%) reported that they intend to continue using the JSANDS system to register neonatal deaths and stillbirths in the future. All variables in JSANDS had complete data with no missing values. CONCLUSIONS: The performance of JSANDS in registering all stillbirths and neonatal deaths as well as their causes was excellent. Almost all attributes and indicators of JSANDS functionality were rated excellent. JSANDS can be scaled up to cover all maternity hospitals in Jordan. The potential for scaling up the system is very high for many reasons, including its usefulness, simplified stillbirth and neonatal death review tools, and ease of the reporting process.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".