Promoting data-driven decision-making in Jordan: strengthening national health information system and achieving consensus on core set of health system indicators
Bibliographic record
Abstract
BACKGROUND: A well-functioning health information system (HIS) is foundational for strong health systems and the achievement of the Sustainable Development Goals. In Jordan, the national HIS, overseen by the Ministry of Health, faces challenges related to overlapping data collection, data availability gaps, and operational inefficiencies which compromise effective decision-making. This study aims to promote data-driven decision-making in Jordan by assessing the existing HIS and fostering consensus on a standardized set of indicators for core health system functions, maternal, child and adolescent health, and refugee health. METHODS: A multifaceted stepwise approach was adopted, encompassing the following steps: baseline assessment of HIS, compilation of a comprehensive list of candidate indicators, consensus meetings to prioritize and validate the indicators, and development of procedure manual for standardizing the shortlisted indicators. RESULTS: The baseline assessment of HIS identified areas for improvement at the following levels: governance and planning; infrastructure and resources; data management; and institutional capacity to support data-driven decision-making. Of 4,120 indicators reviewed from international sources and 215 from Jordan's indicators inventory, 415 candidate indicators were compiled and categorized into three priority thematic areas: core health and health systems indicators (n = 167), maternal, child and adolescent health indicators (n = 137), and refugee health indicators (n = 111). Fifteen stakeholders took part in the first consensus meeting, 14 in the second, and 10 in the third meeting. Utilizing a criterion-based ranking system, participants independently rated each candidate indicator against three criteria: Importance, Feasibility, and Actionability. The shortlisted indicators were subsequently validated against the criterion 'retain'. This process resulted in a final validated list of indicators, comprising 55 core health systems indicators (33 of which are reported in Jordan); 40 maternal, child and adolescent health indicators (21 of which are reported in Jordan); and 26 refugee health indicators (none of which are reported in Jordan). Participants also suggested indicators to be added to each thematic areas. Three procedure manuals were developed and validated, corresponding to the three thematic areas. CONCLUSION: Findings from this study can contribute to the broader discourse on HIS reforms in Jordan, emphasizing the need for ongoing efforts to enhance data quality, stakeholder collaboration, and infrastructure.
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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 | 0.402 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".