Developing a tool to assess the skills to perform a health technology assessment
Bibliographic record
Abstract
BACKGROUND: Health technology assessment (HTA) brings together evidence from various disciplines while using explicit methods to assess the value of health technologies. In resource-constrained settings, there is a growing demand to measure and develop specialist skills, including those for HTA, to aid the implementation of Universal Healthcare Coverage. The purpose of this study was twofold: a) to find validated tools for the assessment of the technical capacity to conduct a HTA, and if none were found, to develop a tool, and b) to describe experiences of its pilot. METHODS: First, a mapping review identified tools to assess the skills to conduct a HTA. A medical librarian conducted a comprehensive search in four databases (MEDLINE, Embase, Web of Science, ERIC). Then, incorporating results from the mapping and following an iterative process involving stakeholders and experts, we developed a HTA skills assessment tool. Finally, using an online platform to gather and analyse responses, in collaboration with our institutional partner, we piloted the tool in Ghana, and sought feedback on their experiences. RESULTS: The database search yielded 3871 records; fifteen those were selected based on a priori criteria. These records were published between 2003 and 2018, but none covered all technical skills to conduct a HTA. In the absence of an instrument meeting our needs, we developed a HTA skill assessment tool containing four sections (general information, core and soft skills, and future needs). The tool was designed to be administered to a broad range of individuals who would potentially contribute to the planning, delivery and evaluation of HTA. The tool was piloted with twenty-three individuals who completed the skills assessment and shared their initial impressions of the tool. CONCLUSIONS: To our knowledge, this is the first comprehensive tool enabling the assessment of technical skills to conduct a HTA. This tool allows teams to understand where their individual strengths and weakness lie. The tool is in the early validation phases and further testing is needed. TRIAL REGISTRATION: Not applicable.
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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 | 0.394 | 0.222 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".