Does the health information system in Jordan support equity to improve health outcomes? Assessment and recommendations
Bibliographic record
Abstract
BACKGROUND: This study is based on extensive evidence-based assessments. The aim of this paper is to evaluate how well Jordan's health information system (HIS) incorporates social determinants of health inequity (SDHI) and to propose suggestions for future actions. METHODS: An extensive evidence-based assessment was performed. A meta-synthesis of the inclusion of the SDHI in the HIS in Jordan was conducted. After searching and shortlisting, 23 papers were analyzed using Atlas.ti 9.0 employing thematic analysis technique. RESULTS: The HIS in Jordan is quite comprehensive, comprising numerous data sources, various types of information, and data from multiple producers and managers. Nevertheless, the HIS confronts several obstacles and fails to ensure the timely and secure publication of available data. The assessment of the inclusion of the SDHI in the HIS showed that the HIS allows for the measurement of progress in relation to social policies and actions but has a very limited database for supporting the inclusion of health inequity measures. One reason for the difficulty in identifying fairness is that certain crucial information necessary for this task cannot be obtained through the available institutional HIS or population survey tools. Additionally, relevant modules for fairness may be missing from population surveys, possibly due to a failure to fully utilize the capabilities of the institutional HIS. CONCLUSION: There are opportunities to make use of Jordan's dedication to fairness and its already established strong HIS. Some social determinants of health exist in the HIS, but much more data, information, and effort are needed to integrate the SDHI into the Jordanian HIS. A proposal from a regional initiative has put forward a comprehensive set of indicators for integrating SDHI into HIS, which could aid in achieving health equity in Jordan.
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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 | 0.056 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".