Mentoring a developing health technology assessment initiative in Romania: An example for countries with limited experience of assessing health technology
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to assist and facilitate introduction and development of a health technology assessment (HTA) program in Romania. METHODS: Mentoring of an initiative group in Romania was provided by an HTA program in Canada. Mentoring activities included provision of HTA materials, participation in local seminars, facilitating contact with HTA and funding organizations, and in-house training of a professional from Romania. RESULTS: Since 1998, when the relationship was initiated, the Romanian group has been successful in developing an understanding of HTA and awareness of its utility among various decision-makers in the health system. Currently, although the need for HTA in Romania exists and interest in developing this activity has been officially expressed, HTA is still early in its development phase. The mentoring support helped to identify and define the need for HTA in Romania. Continuation of the existing relationship can be expected to strengthen the expertise in this country. However, while mentoring has been a valuable activity, it is not, by itself, sufficient to ensure development of an HTA program in Romania. The actions and decisions that could lead to implementing HTA in Romania depend on the local context. CONCLUSIONS: Mentoring services assisted the initiative group in promoting HTA in Romania. The implementation of HTA in Romania has not happened yet, and efforts need to continue to sustain the existing momentum. However, success in implementing an HTA program will depend on essential factors such as local political, economical, and educational support for this initiative and others like it.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".