Personal data governance and privacy in digital reproductive, maternal, newborn, and child health initiatives in Palestine and Jordan: a mapping exercise
Bibliographic record
Abstract
Introduction: There is a rapid increase in using digital technology for strengthening delivery of reproductive, maternal, newborn, and child health (RMNCH) services. Although digital health has potentially many benefits, utilizing it without taking into consideration the possible risks related to the security and privacy of patients' data, and consequently their rights, would yield negative consequences for potential beneficiaries. Mitigating these risks requires effective governance, especially in humanitarian and low-resourced settings. The issue of governing digital personal data in RMNCH services has to date been inadequately considered in the context of low-and-middle-income countries (LMICs). This paper aimed to understand the ecosystem of digital technology for RMNCH services in Palestine and Jordan, the levels of maturity of them, and the implementation challenges experienced, particularly concerning data governance and human rights. Methods: A mapping exercise was conducted to identify digital RMNCH initiatives in Palestine and Jordan and mapping relevant information from identified initiatives. Information was collected from several resources, including relevant available documents and personal communications with stakeholders. Results: A total of 11 digital health initiatives in Palestine and 9 in Jordan were identified, including: 6 health information systems, 4 registries, 4 health surveillance systems, 3 websites, and 3 mobile-based applications. Most of these initiatives were fully developed and implemented. The initiatives collect patients' personal data, which are managed and controlled by the main owner of the initiative. Privacy policy was not available for many of the initiatives. Discussion: Digital health is becoming a part of the health system in Palestine and Jordan, and there is an increasing use of digital technology in the field of RMNCH services in both countries, particularly expanding in recent years. This increase, however, is not accompanied by clear regulatory policies especially when it comes to privacy and security of personal data, and how this data is governed. Digital RMNCH initiatives have the potential to promote effective and equitable access to services, but stronger regulatory mechanisms are required to ensure the effective realization of this potential in practice.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".