Learning needs of patients with congestive heart failure.
Bibliographic record
Abstract
BACKGROUND: It is suggested that more effective and efficient educational intervention can be created by matching the program to patient learning needs. Previous attempts to determine the learning needs of patients with congestive heart failure (CHF) find all types of information endorsed as very important to learn. OBJECTIVES: To increase differentiation between patients' ratings of information needs by modifying the CHF Patient Learning Needs Inventory (CHFPLNI) and examined predictors of learning needs. METHODS: Thirty-four inpatients with CHF from the Toronto General Hospital, Toronto, Ontario completed the modified CHFPLNI and rank ordered the perceived importance of eight categories of CHF knowledge measured by the CHFPLNI. Patients also completed measures of emotional distress, fatigue, health beliefs, locus of control and current CHF knowledge. RESULTS: Ratings across all information categories were similar (M=4.4-5.3/7) and highly correlated (r=0.52-0.87). Patients indicated information on medication, cardiovascular anatomy and physiology, and treatment were the most important to learn on both the CHFPLNI and by rank ordering. Higher fatigue was correlated with information needs on diet (r=0.37), activity (r=0.37), psychological (r=0.38) and risk (r=0.37) factors. No other variables consistently predicted learning needs. CONCLUSIONS: Changing the format of the CHFPLNI did not increase the differentiation of patients' ratings across information categories. The assessment of patients' learning needs using extensive questionnaires does not appear warranted because simple rank ordering obtained similar information. Individuals who are more fatigued wanted more information on those aspects of care that they managed on a day-to-day basis.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".