Screening for distress and needs: Findings from a multinational validation of the Adolescent and Young Adult Psycho‐Oncology Screening Tool with newly diagnosed patients
Bibliographic record
Abstract
OBJECTIVE: Adolescents and young adults (AYAs) diagnosed with cancer commonly experience elevated psychological distress and need appropriate detection and management of the psychosocial impact of their illness and treatment. This paper describes the multinational validation of the Distress Thermometer (DT) for AYAs recently diagnosed with cancer and the relationship between distress and patient concerns on the AYA-Needs Assessment (AYA-NA). METHODS: = 3.8) from Australia (n = 111), Canada (n = 67), the UK (n = 85) and the USA (n = 25) completed the DT, AYA-NA, Hospital Anxiety Depression Scale (HADS) and demographic measures within 3 months of diagnosis. Using the HADS as a criterion, receiver operating characteristics analysis was used to determine the optimal cut-off score and meet the acceptable level of 0.70 for sensitivity and specificity. Correlations between the DT and HADS scores, prevalence of distress and AYA-NA scores were reported. RESULTS: The DT correlated strongly with the HADS-Total, providing construct validity evidence (r = 0.65, p < 0.001). A score of 5 resulted in the best clinical screening cut-off on the DT (sensitivity = 82%, specificity = 75%, Youden Index = 0.57). Forty-two percent of AYAs scored at or above 5. 'Loss of meaning or purpose' was the AYA-NA item most likely to differentiate distressed AYAs. CONCLUSIONS: The DT is a valid distress screening instrument for AYAs with cancer. The AYA-POST (DT and AYA-NA) provides clinicians with a critical tool to assess the psychosocial well-being of this group, allowing for the provision of personalised support and care responsive to individuals' specific needs and concerns.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".