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Record W2118248720 · doi:10.1071/nb12089

Knowledge and beliefs about alcohol consumption, longer-term health risks, and the link with cancer in a sample of Australian adults

2013· article· en· W2118248720 on OpenAlexaff
Trish Cotter, Donna Perez, Sally Dunlop, James Kite, Clive Gaskin

Bibliographic record

VenueNew South Wales Public Health Bulletin · 2013
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsInstitute of Cancer Research
FundersEurostars
KeywordsAlcohol consumptionMedicineEnvironmental healthConsumption (sociology)Telephone interviewAlcoholGerontology

Abstract

fetched live from OpenAlex

AIM: This study explores knowledge and beliefs about longer-term health risks related to alcohol consumption among Australian adults. METHODS: Data were drawn from the 2009 Cancer Institute NSW Lifestyle and Cancer Survey, a telephone survey of adults in NSW. Participants (n=1255) were asked about their alcohol consumption, knowledge of the Australian guidelines (revised in 2009), and personal perceptions and beliefs about longer-term health risks from alcohol consumption. RESULTS: Seventy-eight percent of the sample drank alcohol either occasionally or weekly, with 37% of drinkers drinking above the current Australian guidelines (two standard drinks on any day). Two-thirds (67%) correctly nominated the maximum number of standard drinks per day that met the current Australian guidelines, and a similar proportion (64%) agreed that regular moderate alcohol consumption can have serious health consequences in the longer term. Knowledge of the guidelines and longer-term health consequences was lower for drinkers, especially those drinking above the guidelines. Less than half (48%) of the participants were aware that drinking alcohol could cause cancer and 51% were aware that limiting alcohol intake helps prevent cancer. CONCLUSION: The current Australian guidelines, the longer-term health risks and the link with cancer are not well understood, especially by those who drink frequently and above the guidelines.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.133
GPT teacher head0.391
Teacher spread0.258 · 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

Citations12
Published2013
Admission routes1
Has abstractyes

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