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Record W2081930326 · doi:10.1188/14.onf.41-05ap

Alcohol Use Assessment in Young Adult Cancer Survivors

2014· article· en· W2081930326 on OpenAlexaff
Katherine Breitenbach, Marc Epstein-Reeves, Eileen Danaher Hacker, Colleen Corte, Mariann R. Piano

Bibliographic record

VenueOncology nursing forum · 2014
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsCanadian Hospice Palliative Care Association
Fundersnot available
KeywordsMedicineYoung adultAlcohol consumptionCancerConsumption (sociology)AlcoholGerontologyEnvironmental healthOncologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.399
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2014
Admission routes1
Has abstractyes

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