Pilot approach to analyzing patient and citizen involvement in health technology assessment in four diverse low- and middle-income countries
Bibliographic record
Abstract
BACKGROUND: In low- and middle-income countries (LMICs) striving to achieve universal health coverage, the involvement of different stakeholders in formal or informal ways in health technology assessment (HTA) must be culturally and socially relevant and acceptable. Challenges may be different from those seen in high-income countries. In this article, we aimed to pilot a questionnaire for uncovering the context-related aspects of patient and citizen involvement (PCI) in LMICs, collecting experiences encountered with PCI, and identifying opportunities for patients and citizens toward contributing to local decision- and policy-making processes related to health technologies. METHODS: Through a collaborative, international multi-stakeholder initiative, a questionnaire was developed for describing each LMIC's healthcare system context and the emergence of opportunities for PCI relating to HTA. The questionnaire was piloted in the first set of countries (Brazil, Indonesia, Nigeria, and South Africa). RESULTS: The questionnaire was successfully applied across four diverse LMICs, which are at different stages of using HTA to inform decision making. Only in Brazil, formal ways of PCI have been defined. In the other countries, there is informal influence that is contingent upon the engagement level of patient and citizen advocacy groups (PCAGs), usually strongest in areas such as HIV/AIDS, TB, oncology, or rare diseases. CONCLUSIONS: The questionnaire can be used to analyze the options for patients and citizens to participate in HTA or healthcare decision making. It will be rolled out to more LMICs to describe the requirements and opportunities for PCI in the context of LMICs and to identify possible routes and methodologies for devising a more systematic and formalized PCI in LMICs.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".