Relationships among unmet needs, depression, and anxiety in non–advanced cancer patients
Bibliographic record
Abstract
Introduction: In oncology settings, less attention is given to patients’ unmet needs and to existential and emotional distress compared to physical symptoms. We aimed to evaluate correlations between unmet needs and emotional distress (self-reported anxiety and depression) in a consecutive cohort of cancer patients. The influence of sociodemographic and clinical factors was also considered. Methods: A total of 300 patients with cancer recruited from an outpatient Supportive Care Unit of a Comprehensive Cancer Centre completed the Need Evaluation Questionnaire and the Edmonton Symptom Assessment System (ESAS). Unmet needs covered 5 distinct domains (informational, care/assistance, relational, psychoemotional, and material). Results: After removal of missing data, we analyzed data from 258 patients. Need for better information on future health concerns (43%), for better services from the hospital (42%), and to speak with individuals in the same condition (32%) were the most frequently reported as unmet. Based on the ESAS, 27.2% and 17.5% of patients, respectively, had a score of anxiety or depression >3 and needed further examination for psychological distress. Female patients had significantly higher scores for anxiety ( p < 0.001) and depression ( p = 0.008) compared to male patients. Unmet needs were significantly correlated with both anxiety ( r s = 0.283) and depression ( r s = 0.284). Previous referral to a psychologist was significantly associated with depression scores ( p = 0.015). Results were confirmed by multiple regression analysis. Conclusions: Screening for unmet needs while also considering sociodemographic and clinical factors allows early identification of cancer patients with emotional distress. Doing so will enable optimal management of psychological patient-reported outcomes in oncology settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".