Alcohol Use Assessment in Young Adult Cancer Survivors
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To determine whether oncology practitioners assess for alcohol consumption rates and usage patterns among young adult cancer survivors, and to determine drinking patterns and frequency of alcoholic beverage consumption among young adult cancer survivors. DESIGN: Retrospective chart review. SETTING: Two outpatient cancer clinics. SAMPLE: 77 young adult survivors of childhood cancer aged 18-30 years. METHODS: Charts were selected from June to December 2009 and data were extracted using a structured questionnaire. MAIN RESEARCH VARIABLES: Oncology practitioner assessment of alcohol use and alcohol consumption of young adult cancer survivors. FINDINGS: Alcohol screening was conducted for 48 participants. No significant differences were noted in most variables between those not screened for alcohol use and those screened for alcohol use. Of the 48 screened for alcohol use, 30 reported "no use." For the 18 who reported alcohol use, the terms used to describe the frequency varied and were vague. CONCLUSIONS: The key finding of the study was that screening and documentation of alcohol consumption was poorly and inconsistently performed in the authors' sample of young adult cancer survivors. IMPLICATIONS FOR NURSING: Similar to healthy young adults aged 18-30 years, young adult cancer survivors are at a developmental age where it is likely they will engage in unhealthy drinking; therefore, they should be screened for alcohol use and binge drinking. Practitioners can incorporate simple, short questions into health assessment visits that allow them to screen for unhealthy alcohol use.
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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 | 0.006 |
| 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) | 0.001 | 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".