A validation study of the modified Chinese version of the integrated palliative care outcome scale (IPOS) among patients with advanced illness in Hong Kong
Bibliographic record
Abstract
BACKGROUND: In order to conduct holistic person-centred assessment, monitoring and research in progressive illness, a valid tool is required that captures the specific symptoms and concerns faced by patients and families. While the Integrated Palliative care Outcome Scale (IPOS) is widely used, its cultural applicability for Chinese populations remains unestablished. METHODS: This psychometric validation study aimed to culturally adapt the IPOS and evaluate its psychometric properties in Chinese-speaking populations. Conducted in oncology and palliative care settings in Hong Kong, the study comprised two phases. Phase I: Traditional Chinese IPOS translation with face/content validity testing through cognitive interviews(7 patients with advanced illness, 6 caregivers, 15 healthcare professionals) and expert panel meeting. Phase II: psychometric testing with 236 patient-caregiver dyads and 21 professionals. Analyses included exploratory/confirmatory factor analyses, convergent validity with Edmonton Symptom Assessment System (ESAS) item pairs, divergent validity with State Hope Scale (SHS), predictive validity with EuroQoL5-dimensional 5-level Scale(EQ-5D-5L)/Palliative Performance Scale-version2(PPSv2), known-group validity, reliability, responsiveness and feasibility. RESULTS: The Modified Traditional Chinese IPOS(MC-IPOS) was formed with swelling of limbs and difficulty in sleeping added, demonstrated high face/content validity. EFA extracted 5 factors with CFA indicating marginally acceptable indices(CFI=.91, RMSEA/SRMR=.06); the 1-factor(scree plot suggested) and theoretical 3-factor solutions exhibited poor fit, leading to further item-level analysis. Convergent validity was established with significant correlations(.35≤ρ≤.79) to ESAS item pairs; divergent validity unconfirmed. Known-group validity was evidenced by significant score differences(total scores and 17/19 item scores) observed between stable and unstable/deteriorating patients. Specific MC-IPOS items can predict corresponding EQ-5D-5L items, while PPSv2 can predict most MC-IPOS items. Internal consistency(α=.86) was good, inter-rater reliability was acceptable(>80% items .2w≤.61 across all dyads/timepoints), and test-retest reliability(70% items kw≥.60) was good. MC-IPOS was responsive in improved group(z=-2.49, p<.001), feasible and acceptable(patient mean completion time 8.39 minutes, 86.9% reported acceptable length). CONCLUSIONS: The MC-IPOS has adequate properties for implementation to improve routine palliative care practice and research among Chinese-speaking populations.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".