Global research on patient involvement in health technology assessment: a bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: Patient involvement in health technology assessment (HTA) has been extensively explored and implemented in high-income countries, but little is known about it in low- and middle-income countries (LMICs). This study aimed to provide a comprehensive picture of the current state and trends of patient involvement in HTA research, which can inform future research in the LMICs. METHODS: Publications on patient involvement in HTA from January 1, 1900, to December 31, 2023, were retrieved from the core databases of the Web of Science. We applied a bibliometric analysis to reveal the collaboration patterns, hot topics, and evolution of the research field. Co-occurrence, clustering, citation, and burst analyses were performed using VOSviewer and CiteSpace, with results visualized for interpretation. RESULTS: A total of 175 articles were eligible for inclusion. The first valid article was published in 2000. The number of publications has increased since 2011. The most productive countries and institutions were Canada and McMaster University. The studies focused on five hot topics: patient preferences, priority setting, qualitative research, drug development, and hospital-based HTA. The burst analysis revealed that priority setting and cost effectiveness were the research frontiers. CONCLUSIONS: While patient involvement in HTA research has gained increasing attention, the research conducted in the LMICs remain limited. It is recommended that LMICs participate in international research collaborations, and focus on the five hot topics and emerging frontiers to advance both their research capacity and practical implementations.
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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 | 0.018 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.104 | 0.389 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".