An analysis of the construct validity and responsiveness of the ICECAP-SCM capability wellbeing measure in a palliative care hospice setting
Bibliographic record
Abstract
BACKGROUND: For outcome measures to be useful in health and care decision-making, they need to have certain psychometric properties. The ICECAP-Supportive Care Measure (ICECAP-SCM), a seven attribute measure (1. Choice, 2. Love and affection, 3. Physical suffering, 4. Emotional suffering, 5. Dignity, 6. Being supported, 7. Preparation) developed for use in economic evaluation of end-of-life interventions, has face validity and is feasible to use. This study aimed to assess the construct validity and responsiveness of the ICECAP-SCM in hospice inpatient and outpatient settings. METHODS: A secondary analysis of data collated from two studies, one focusing on palliative care day services and the other on constipation management, undertaken in the same national hospice organisation across three UK hospices, was conducted. Other quality of life and wellbeing outcome measures used were the EQ-5D-5L, McGill Quality of Life Questionnaire - Expanded (MQOL-E), Patient Health Questionnaire-2 (PHQ-2) and Palliative Outcomes Scale Symptom list (POS-S). The construct validity of the ICECAP-SCM was assessed, following hypotheses generation, by calculating correlations between: (i) its domains and the domains of other outcome measures, (ii) its summary score and the other measures' domains, (iii) its summary score and the summary scores of the other measures. The responsiveness of the ICECAP-SCM was assessed using anchor-based methods to understand change over time. Statistical analysis consisted of Spearman and Pearson correlations for construct validity and paired t-tests for the responsiveness analysis. RESULTS: Sixty-eight participants were included in the baseline analysis. Five strong correlations were found with ICECAP-SCM attributes and items on the other measures: four with the Emotional suffering attribute (Anxiety/depression on EQ-5D-5L, Psychological and Burden on MQOL-E and Feeling down, depressed or hopeless on PHQ-2), and one with Physical suffering (Weakness or lack of energy on POS-S). ICECAP-SCM attributes and scores were most strongly associated with the MQOL-E measure (0.73 correlation coefficient between summary scores). The responsiveness analysis (n = 36) showed the ICECAP-SCM score was responsive to change when anchored to changes on the MQOL-E over time (p < 0.05). CONCLUSIONS: This study provides initial evidence of construct validity and responsiveness of the ICECAP-SCM in hospice settings and suggests its potential for use in end-of-life care research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".