IMPLEMENTATION OF LOCAL/HOSPITAL-BASED HEALTH TECHNOLOGY ASSESSMENT INITIATIVES IN LOW- AND MIDDLE-INCOME COUNTRIES
Bibliographic record
Abstract
OBJECTIVES: The objective of this study is to review the implementation of health technology assessment (HTA) at the local and hospital levels in low- and middle-income countries (LMIC). This review will provide a starting point for identifying the conditions for HTA implementation in hospitals in LMIC through the analysis of experiments conducted in these countries. METHODS: A systematic review of the literature was conducted to document the local-/hospital-level HTA experiments performed in LMIC. RESULTS: This systematic review showed that few experiments of local HTA in LMIC have been published to date, with only five articles found in our survey. These documents report studies of clinical effectiveness and economic evaluation at the local level in certain Asian and Latin American countries. In addition, pharmaceuticals and medical devices were the most common topics covered by HTA at the local level in these countries. CONCLUSIONS: Currently, HTA plays an increasingly important role in healthcare systems in supporting decision making for healthcare policies and practices. This systematic review contributes to identify priorities in the process and methodology of HTA implementation at the local/hospital level in LMIC. The paucity of HTA in LMIC is often assumed to be due to the lack of formally tasked HTA agencies, to politics and to shortage of resources.
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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 | 0.038 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".