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Record W2140842539 · doi:10.12968/bjon.2012.21.8.474

Nurses' role in managing alcohol misuse among adolescents

2012· article· en· W2140842539 on OpenAlexaff
Claire L. Kiernan, Aislinn Ni Fhearail, Imelda Coyne

Bibliographic record

VenueBritish Journal of Nursing · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsTrinity College
Fundersnot available
KeywordsHealth professionalsMedicineHealth promotionOccupational safety and healthSuicide preventionNursingHealth careInjury preventionEnvironmental healthPoison controlPublic healthPsychiatryPsychologyPolitical science

Abstract

fetched live from OpenAlex

Over the past decade, there has been an increase in the amount of alcohol consumed by young people, aged 11-17 years, in the UK and Ireland, which has implications for all health professionals caring for adolescents. Alcohol misuse is increasingly common among adolescents and is a significant concern for families, communities and society. Health professionals need to be aware of the dangers involved with underage drinking, how to recognise the signs of alcohol misuse, and how to intervene appropriately. Over the past few years, there has been a noticeable increase in the number of adolescents presenting to emergency departments (EDs) owing to alcohol-related injuries. This increase means that all nurses and other health professionals are suitably placed to provide education and support to adolescents who are consuming excessive alcohol. Regular alcohol misuse can lead to adverse health outcomes, and therefore nurses need to take an active role in health promotion to ensure that adolescents are aware of the associated dangers. This article summarises the harmful effects of underage drinking, the influencing factors and outlines the current guidelines on alcohol misuse in young people. It discusses strategies that nurses can use in the ED setting, and all healthcare settings, to motivate adolescents to change health-damaging behaviours.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.053
GPT teacher head0.423
Teacher spread0.371 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2012
Admission routes1
Has abstractyes

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