Resilience as a Factor Influencing Psychological Distress Experience in Patients with Neuro-Oncological Disease
Bibliographic record
Abstract
Cancer causes psychological distress. Approximately one-third of all patients with cancer suffer from distress requiring psycho-oncological treatment. Examining factors contributing to their distress can inform approaches to counteracting them. Among such factors, resilience is considered to be a psychological adaptive capacity resulting from complex genetic, epigenetic, psychological, and environmental influences. For that reason, we investigated resilience as a factor of psychological distress experience among patients with neuro-oncological disease. To assess distress among patients with neuro-oncological diseases, we performed electronic psycho-oncological screening in the Department of Neurosurgery at Tübingen University Hospital (n = 100) following tumor surgery (T0) using the Resilience Scale 13, the Hornheider Screening Instrument, the Patient Health Questionnaire-2, the Generalized Anxiety Disorder Scale-2, and the Distress Thermometer, all administered on tablets. Follow-up was done 6 months after (T1). The distress of patients with neuro-oncological disease decreased significantly after 6 months (p < 0.01). Most patients (87%) showed moderate to high resilience. Although significant correlations with distress are measurable at the T0 time point (ρ = −0.318 **, p < 0.01), no significant correlations were observed at T1. Thus, resilience seems to significantly impact distress in the acute phase of the neuro-oncological disease. For clinical practice, our findings suggest that resilience-focused screening can provide useful information about patients at risk of experiencing distress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".