Emotional vitality in caregivers: application of Rasch Measurement Theory with secondary data to development and test a new measure
Bibliographic record
Abstract
OBJECTIVE: To describe the practical steps in identifying items and evaluating scoring strategies for a new measure of emotional vitality in informal caregivers of individuals who have experienced a significant health event. DESIGN: The psychometric properties of responses to selected items from validated health-related quality of life and other psychosocial questionnaires administered four times over a one-year period were evaluated using Rasch Measurement Theory. SETTING: Community. SUBJECTS: A total of 409 individuals providing informal care at home to older adults who had experienced a recent stroke. MAIN MEASURES: Rasch Measurement Theory was used to test the ordering of response option thresholds, fit, spread of the item locations, residual correlations, person separation index, and stability across time. RESULTS: Based on a theoretical framework developed in earlier work, we identified 22 candidate items from a pool of relevant psychosocial measures available. Of these, additional evaluation resulted in 19 items that could be used to assess the five core domains. The overall model fit was reasonable (χ(2) = 202.26, DF = 117, p = 0.06), stable across time, with borderline evidence of multidimensionality (10%). Items and people covered a continuum ranging from -3.7 to +2.7 logits, reflecting coverage of the measurement continuum, with a person separation index of 0.85. Mean fit of caregivers was lower than expected (-1.31 ±1.10 logits). CONCLUSION: Established methods from the Rasch Measurement Theory were applied to develop a prototype measure of emotional vitality that is acceptable, reliable, and can be used to obtain an interval level score for use in future research and clinical settings.
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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 | 0.056 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".