EVALUATION OF PATIENT INVOLVEMENT IN A HEALTH TECHNOLOGY ASSESSMENT
Bibliographic record
Abstract
OBJECTIVES: We sought to evaluate patient involvement (consultation and direct participation) in the assessment of alternative measures to restraint and seclusion among adults in short-term hospital wards (in psychiatry) and long-term care facilities for the elderly. METHODS: We conducted individual semi-structured interviews with thirteen stakeholders: caregivers, healthcare managers, patient representatives, health technology assessment (HTA) unit members, researchers, and members of the local HTA scientific committee. Data were collected until saturation. We carried out content analysis of two HTA reports and four other documents that were produced in relation with this HTA. We also used field notes taken during formal meetings and informal discussions with stakeholders. We performed thematic analysis based on a framework for assessing patient involvement in HTA. We then triangulated data. RESULTS: For the majority of interviewees, patient consultation enriched the content of the HTA report and its recommendations. This also made it possible to suggest other alternatives that could reduce the use of restraint and seclusion and helped confirm some views and comments from healthcare professionals consulted in this HTA. The direct participation of patient representatives enabled rephrasing of some findings so as to bring the patient perspective to the HTA report. CONCLUSIONS: Patient consultation was seen as having directly influenced the content of the HTA report while direct participation made it possible to rephrase some findings. This is one of few studies to assess the impact of patient involvement in HTA and more such studies are needed to identify the best ways to improve the input of such involvement.
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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 | 0.152 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".