Risk factors for low self‐care self‐efficacy in cancer survivors: Application of latent profile analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: To identify subgroups of cancer patients with distinct self-care self-efficacy profiles and to explore factors that can be used to predict those at risk of low self-efficacy. DESIGN: A secondary analysis of data pooled from two cross-sectional surveys was performed. METHODS: In total, 1,367 Chinese cancer survivors were included in the analysis. Latent profile analysis (LPA) was performed to categorize participants into latent subgroups with distinct self-efficacy profiles. Multinomial logistic regression was conducted to identify predictors of self-care self-efficacy subgroup classification. RESULTS: We identified three distinct subgroups: low, medium and high self-care self-efficacy. Patients with the "low" profile, which was characterized by a low education level, single marital status, complications, late cancer stage and a lower level of social support, had the poorest self-care behaviour.
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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 it