Questionnaires Designed to Assess Knowledge of Heart Failure Patients
Bibliographic record
Abstract
BACKGROUND: The objectives of this systematic review were (1) to describe the psychometric properties of questionnaires designed to assess knowledge of heart failure (HF) patients and (2) to identify the most applicable questionnaire to assess knowledge of HF patients. METHODS: A literature search of electronic databases was conducted from database inception to March 2014. Eligible articles included studies describing the development and psychometric testing of questionnaires designed to assess HF patients' knowledge. Outcomes were based on the quality criteria for measurement properties of health status questionnaires. Articles were considered for inclusion by 2 authors independently. RESULTS: Overall, 12 articles were included, of which 4 (33.33%) were considered "good" quality. Twelve original English-language instruments were identified. Content validity was described in 10 studies and presented positive ratings in all of them (83.33%); internal consistency in 10 studies and positive ratings in 5 (41.66%); construct validity in 5 and positive ratings in 4 (33.33%); responsiveness in 3 and positive ratings in 2 (16.66%); and reproducibility and floor effects in 1 with positive ratings. Based on our criteria, the Atlanta HF Knowledge Test was identified as the most applicable questionnaire to assess knowledge of HF patients. CONCLUSION: Psychometric properties of questionnaires assessing HF patients' knowledge are poorly described in the literature. Although we identified the Atlanta HF Knowledge Test as the most promising instrument, it has methodological limitations. We recommend the researchers to use the questionnaire that best serves their research question and context.
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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 | 0.026 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".