Electronic Health Literacy, Psychological Distress, and Quality of Life in Urological Cancer Patients: A Longitudinal Study During Transition from Inpatient to Outpatient Care
Bibliographic record
Abstract
Urological cancers are associated with reduced quality of life and high psychological burden, yet affected patients receive less psychosocial support than other cancer groups. Electronic health literacy (eHL) may facilitate independent access to resources, but its role for psychological outcomes and quality of life in this group is unclear. This study examined associations between eHL, psychological symptoms, and quality of life during transition from inpatient to outpatient care. A prospective, single-centre observational study was conducted. Eligible inpatients (urological cancer, Distress Thermometer ≥5 and/or request for psycho-oncological support) received an initial psycho-oncology consultation and completed surveys during inpatient treatment (T1) and three months later (T2). Measures included socio-demographics, PO-BADO, eHL (eHEALS), distress, depression (PHQ-2), anxiety (GAD-2), and quality of life (EORTC QLQ-C30). Of 108 patients completing T1, 71 completed T2. After controlling for age, eHL was not significantly associated with distress, depression, anxiety, or quality of life. Age did not moderate these relationships. In this sample, eHL showed no significant associations with psychological outcomes or quality of life. However, higher age was linked to lower eHL, suggesting that older patients may face barriers to digital health engagement. Age-related differences in eHL should be considered when designing digital support services for urological cancer patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".