Psychometric Validation of Sheffield Profile for Assessment and Referral to Care (SPARC) in Korean Cancer Patients
Bibliographic record
Abstract
PURPOSE: Identifying the palliative care needs of patients with advanced cancer is important for maintaining quality of life and timely transition to palliative care. We aimed to validate the Korean Sheffield Profile for Assessment and Referral for Care (K-SPARC) in such patients and establish its psychometric properties, including reliability, validity, and responsiveness to change. Materials and Methods: We used the forward-back translated version of SPARC, which was verified through a pilot study, to assess the palliative care needs of patients with advanced cancer. Reliability was evaluated by internal consistency using Cronbach's alpha coefficients and test-retest reliability. Criterion validity was analyzed against other questionnaires, including the Korean versions of the Functional Assessment of Cancer Therapy-General (FACT-G Korean) and Korean versions of the Edmonton Symptom Assessment System (K-ESAS). Factor analysis was used to assess construct validity. RESULTS: Two hundred fifty-nine patients were included from 2019 to 2022. Forty-nine percent of all patients were women, and the median age was 63 years. Cronbach's alpha coefficient (range, 0.642 to 0.903) and test-retest reliability (range, 0.574 to 0.749) indicated acceptable reliability. The correlation coefficients between K-SPARC and FACT-G Korean suggested significant criterion validity. The correlation coefficients for the physical, social, emotional, and functional domains were 0.701, 0.249, 0.718, and 0.511, respectively (p < 0.001, all). Factor analysis demonstrated satisfactory construct validity of the tool. CONCLUSION: This study demonstrated the utility of K-SPARC as an evaluation tool for providing palliative care to patients with advanced cancer through psychometric validation; the tool had good internal consistency, reliability, and acceptable validity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".