Instruments to Measure Acceptability of Information and Acquisition of Knowledge in Patients with Heart Failure
Bibliographic record
Abstract
BACKGROUND: Patients with heart failure suffer from poor health outcomes and require combinations of medications to treat their disease. Providing patients with knowledge through education is one mechanism to help them improve compliance with complicated treatment regimens. METHODS: We developed and tested two instruments. The first instrument, which we call the measure of educational material acceptability (EMA), was designed to help us differentiate between written educational materials according to patients' subjective responses. The second instrument, the knowledge acquisition questionnaire (KAQ), which measures knowledge gained, was designed to determine whether patients understand the rationale and mechanics of their heart failure management. We explored the measurement properties of both instruments. RESULTS: The internal consistency of the EMA was 0.79 (Cronbach's alpha). The internal consistency of the KAQ was 0.61 and its responsiveness, measured using change scores of knowledge before and after an educational intervention, was 0.75. CONCLUSIONS: We have developed instruments that measure acceptability and knowledge acquisition, and that clinicians and investigators involved in heart failure programs may find useful in developing educational material and measuring the impact of their interventions on patients' knowledge.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".