Measuring health literate discharge practices
Bibliographic record
Abstract
Purpose Health literate discharge practices meet patient and family health literacy needs in preparation for care transitions from hospital to home. The purpose of this paper is to measure health literate discharge practices in Ontario hospitals using a new organizational survey questionnaire tool and to perform psychometric testing of this new survey. Design/methodology/approach This survey was administered to hospitals in Ontario, Canada. Exploratory factor analysis and reliability testing were performed. Findings The participation rate of hospitals was 46 percent. Exploratory factor analysis demonstrated that there were five factors. The survey, and each of the five factors, had moderate to high levels of reliability. Research limitations/implications There is a need to expand the focus of further research to examine the experiences of patients and families. Repeating this study with a larger sample would facilitate further survey development. Practical implications Measuring health literate discharge practices with an organizational survey will help hospital managers to understand their performance and will help direct quality improvement efforts to improve patient care at hospital discharge and to decrease hospital readmission. Originality/value There has been little research into how patients are discharged from hospital. This study is the first to use an organizational survey tool to measure health literate discharge practices.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".