HEALTH TECHNOLOGY ASSESSMENT UNIT PROCESSES FOR THE VALIDATION OF AN INFORMATION TOOL TO INVOLVE PATIENTS IN THE SAFETY OF THEIR CARE
Bibliographic record
Abstract
INTRODUCTION: Patients and families play an important role in preventing adverse events. The quality council at our hospital produced a communication tool in considering the main causes of adverse events and requested the health technology assessment (HTA) unit to validate it. OBJECTIVES: Assess the validity of the content of a tablemat sticker as an information tool for hospitalized patients. METHODS: A qualitative validation was first performed with individual interviews and focus groups to evaluate the understanding of the content. The tool was modified and as a second step, a survey was conducted on patients and their families from a surgical care unit to validate their understanding and relevance of the content. RESULTS: From the survey, patients and families found the tablemat attractive and stimulating (97 percent). It encouraged them to communicate with staff about the safety of their care (84 percent). They understood well the objective (79 percent) and text (90 percent), but less for the pictograms (30 percent to 62 percent). The communication and recommendations to avoid falling were good and 99 percent were wearing the medical identification. However, it was not clear that these indicators represented the real concerns of the patients and healthcare staff because no user evaluation was done when developing the tool. CONCLUSIONS: The tool was well understood, but some improvements are needed considering that pictograms were not always well understood and so need careful consideration from patient perspective. The HTA unit recommended conducting an unbiased survey to assess the concerns of patients and professionals to identify the most relevant indicators.
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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 | 0.561 | 0.552 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".