Understanding of the Readiness for HTA Implementation in Jordan as a Step Towards Universal Health Coverage
Bibliographic record
Abstract
As Jordan strives to achieve universal health coverage, the mechanism for determining which health technologies to include in the basket of reimbursed services has become increasingly important. This study sought to understand stakeholder perspectives in the Jordanian health system regarding the readiness and need to implement health technology assessment (HTA) to support decision-making quality and transparency, ensure value for money on health system spending, and support the achievement of universal health coverage. This study used a cross-sectional survey methodology, and a quantitative analysis was conducted. A questionnaire based on the HTA implementation scorecard was administered in-person to capture responses regarding fourteen dimensions. Thirty-one responses from representatives across the Ministry of Health, regulatory authority, and other stakeholders in the national health system were collected. Most respondents were familiar with HTA and there was nearly unanimous agreement on the need for HTA implementation in Jordan. While the perspectives on how the implementation would occur were varied, careful consideration may be warranted in the areas of the legal framework for HTA, the quality of available data, financial constraints, and limited human resource capacity, as Jordan progresses towards implementing HTA on the road to universal healthcare.
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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 | 0.024 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".