Measuring Care Transition Quality for Older Patients with Musculoskeletal Disorders
Bibliographic record
Abstract
OBJECTIVE: The aim of the current study was to examine the ability of a performance measurement scale, the Care Transitions Measure (CTM) adequately to assess the quality of care transitions among a complex population of older musculoskeletal (MSK) rehabilitation patients. METHODS: Fifteen older (aged 60+) patients with MSK disorders were recruited from two inpatient rehabilitation units. A telephone interview was conducted three to four weeks post-discharge; this included the CTM and global questions used for construct validation. To assess inter-rater reliability, the CTM was re-administered to ten subjects in a second interview six to ten days later. Participant comments were recorded in an effort to gauge how respondents understood and interpreted items. RESULTS: The CTM demonstrated acceptable inter-rater reliability for the overall score (intraclass correlation coefficient = 0.77; p = 0.03), in spite of only fair agreement for specific items. The internal consistency was high (Cronbach's alpha = 0.94). The construct validity was supported; however, qualitative data suggest that additional items should be considered for inclusion, and the need for revisions to the wording of the response options and some items. CONCLUSIONS: Although the CTM proved to be reliable for an MSK population, there is a need for modifications to improve the construct validity and utility of the CTM. Recommendations for scale improvement are made. The results of the present study support efforts to improve the outcomes of care transitions, care planning and the overall quality of life for older rehabilitation patients.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".