Psychometric properties of the 45-item supportive care needs survey—partners and caregivers - Dutch (SCNS-P&C45-D) in partners of patients with breast cancer
Bibliographic record
Abstract
OBJECTIVE: To test the psychometric properties of the Dutch 45-item Supportive Care Needs Survey-Partners and Caregivers (SCNS-P&C45-D) among partners of women with breast cancer living in the Netherlands. METHODS: In this cross-sectional validation study, partners of patients with breast cancer were invited to complete a survey on the patient's cancer and the caregiver's level of unmet needs (SCNS-P&C45-D), psychological distress (HADS) and burden (EDIZ). RESULTS: 43% of the invited informal caregivers responded (n = 302). Flooring effects were identified for three items of the SCNS-P&C45-D,which were then deleted from further analysis. The original factor structure and loading pattern of the SCNS-P&C45-D was not replicated. Internal consistency of the SCNS-P&C45-D and all subscales' (emotional and relational needs, health care and illness related needs, practical needs, work and social needs) Cronbach's alpha coefficients exceeded 0.80, the entire measure's Cronbach's alpha is 0.98. Most SCNS-P&C45-D subscales showed moderate correlations with distress and burden from informal care which was in line with expectations based on validity. The domain 'Work and Social needs' showed a high correlation with burden from informal care. Participants reported significantly more or higher unmet needs if they were younger (25.5% vs. 20.3% in older patients, p = 0.004), if diagnosis was less than 1 year ago in one subscale (Health Care and Illness related needs; 19.5% and 18%, p = 0.029, and the total SCNS-P&C45-D; 23.2% vs. 22.4%, p = 0.018). CONCLUSIONS: The SCNS-P&C45-D is able to identify those partners of patients with breast cancer in need and those who are not.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".