Translation and psychometric evaluation of a French version of the Readiness for Hospital Discharge Scale
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To evaluate the reliability and the factor structure of the Readiness for Hospital Discharge Scale-French version. BACKGROUND: The patient's perspective is essential when assessing risk for adverse events at hospital discharge. Developed in the USA, the Readiness for Hospital Discharge Scale is the only instrument that measures an individual's self-perception of readiness before leaving the hospital. A French version of the Readiness for Hospital Discharge Scale was developed and validated. DESIGN: Cross-sectional study. METHODS: A convenience sample of 265 older inpatients from four medical units was selected. The translation and cultural adaptation of the scale involved experts in gerontology and the French language and included back translation. The items were semantically evaluated and pretested in 10 older inpatients. The scale's psychometric properties were internally validated by using confirmatory and exploratory factor analyses. Reliability was assessed by examining the internal consistency of its items. RESULTS: Goodness-of-fit indices of the confirmatory factor analyses were not adequate, but reliability was acceptable (Cronbach's α = 0·80). Exploratory factor analysis of the French version provided results close to those described for the English version, with three similar subscales (physical and emotional readiness, coping with medical treatment and personal care), whereas the initially described Expected Support subscale was not identified in the French version. CONCLUSION: The Readiness for Hospital Discharge Scale-French version appears to be partially consistent with its original English version, but requires additional adaptation to fully take into account the Swiss context and culture to achieve its original aim. RELEVANCE TO CLINICAL PRACTICE: Assessing patient readiness for hospital discharge before leaving hospital could help nurses to improve the discharge planning process and achieve better patient preparedness and care coordination.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".