Screening for Emotional Distress in Cancer Patients: A Systematic Review of Assessment Instruments
Bibliographic record
Abstract
Screening for emotional distress is becoming increasingly common in cancer care. This systematic review examines the psychometric properties of the existing tools used to screen patients for emotional distress, with the goal of encouraging screening programs to use standardized tools that have strong psychometrics. Systematic searches of MEDLINE and PsycINFO databases for English-language studies in cancer patients were performed using a uniform set of key words (eg, depression, anxiety, screening, validation, and scale), and the retrieved studies were independently evaluated by two reviewers. Evaluation criteria included the number of validation studies, the number of participants, generalizability, reliability, the quality of the criterion measure, sensitivity, and specificity. The literature search yielded 106 validation studies that described a total of 33 screening measures. Many generic and cancer-specific scales satisfied a fairly high threshold of quality in terms of their psychometric properties and generalizability. Among the ultrashort measures (ie, those containing one to four items), the Combined Depression Questions performed best in patients receiving palliative care. Among the short measures (ie, those containing five to 20 items), the Center for Epidemiologic Studies-Depression Scale and the Hospital Anxiety and Depression Scale demonstrated adequate psychometric properties. Among the long measures (ie, those containing 21-50 items), the Beck Depression Inventory and the General Health Questionaire-28 met all evaluation criteria. The PsychoSocial Screen for Cancer, the Questionnaire on Stress in Cancer Patients-Revised, and the Rotterdam Symptom Checklist are long measures that can also be recommended for routine screening. In addition, other measures may be considered for specific indications or disease types. Some measures, particularly newly developed cancer-specific scales, require further validation against structured clinical interviews (the criterion standard for validation measures) before they can be recommended.
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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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".