Referee report. For: Health technology assessment capacity at national level in sub-Saharan Africa: an initial survey of stakeholders [version 1; peer review: 1 approved]
Bibliographic record
Abstract
Background: Health technology assessment (HTA) is an effective tool to support priority setting (PS) in health. Stakeholder groups need to understand HTA appropriate to their role and to interpret and critique the evidence produced. We aimed to rapidly assess current health system priorities and policy areas of demand for HTA in Sub-Saharan Africa, and identify key gaps in data and skills to inform targeted capacity building. Methods: We revised an existing survey, delivered it to 357 participants, then analysed responses and explored key themes. Results: There were 51 respondents (14%) across 14 countries. HTA was considered an important and valuable PS tool with a key role in the design of health benefits packages, clinical guideline development, and service improvement. Medicines were identified as a technology type that would especially benefit from the application of HTA. Using HTA to address safety issues (e.g. low-quality medicines) and value for money concerns was particularly highlighted. The perceived availability and accessibility of suitable local data to support HTA varied widely but was mostly considered inadequate and limited. Respondents also noted a need for training support in research methodology and data gathering. Conclusions: While important in raising awareness of HTA as a tool for PS, this study had a low response rate, and that respondents were self-selected. A more refined survey will be developed to support engagement strategies and capacity building.
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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 | 0.011 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.355 | 0.119 |
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".