A framework for action to improve patient and public involvement in health technology assessment
Bibliographic record
Abstract
BACKGROUND: Patient and public involvement (PPI) in the Brazilian Health Technology Assessment (HTA) process occurs in response to a legislative mandate for "social participation." This resulted in some limited patient participation activities, and, therefore, a more systematic approach was needed. The study describes the development of a suggested framework for action to improve PPI in HTA. METHODS: This work used formal methodology to develop a PPI framework based on three-phase mixed-methods research with desktop review of Brazilian PPI activities in HTA; workshop, survey, and interviews with Brazilian stakeholders; and a rapid review of international practices to enact effective patient involvement. Patient partners reviewed the draft framework. RESULTS: According to patient group representatives, their involvement in the Brazilian HTA process is important but could be improved. Different stakeholders perceived barriers, identified values, and made suggestions for improvement, such as expansion of communication, capacity building, and transparency, to support more meaningful patient involvement. The international practices identified opportunities for earlier, more active, and collaborative PPI during all HTA stages, based on values and principles that are relevant for Brazilian patients and the public. These findings were synthesized to design a framework that defines and systematizes actions to support PPI in Brazil, highlighting the importance of evaluating these strategies. CONCLUSIONS: Since the publication of this framework, some of its suggestions are being implemented in the Brazilian HTA process to improve PPI. We encourage other HTA organizations to consider a systematic and planned approach with regular evaluation when pursuing or strengthening involvement practices.
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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 | 0.274 | 0.124 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.018 | 0.056 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.015 | 0.017 |
| 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; 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".