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Record W2972462166 · doi:10.2427/13128

Social determinants of Health and Alcohol consumption in the UK

2022· article· en· W2972462166 on OpenAlexaff
Sheeraz Ali Rajput, Muhammad Owais Aziz, Muhammad A. Siddiqui

Bibliographic record

VenueEpidemiology Biostatistics and Public Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsSaskatchewan Health Authority
Fundersnot available
KeywordsAlcohol consumptionConsumption (sociology)Environmental healthAlcoholSocial determinants of healthMedicinePsychologySociologyPublic healthNursingSocial scienceBiology

Abstract

fetched live from OpenAlex

Addressing the social determinants of health (SDH) and health inequities are essential for successfully combating alcohol-related harm. In U.K, excessive consumption of alcohol is a huge public health concern. An estimated 9 million adults drink at level that increase the risk of harm to their health; 1.6 million adults in England have some degree of alcohol dependence; and of these some 250,000 are believed to be moderately or severely dependent and may benefit from intensive specialist treatment. To be able to devise effective action, it is essential to comprehend these inequities in the healthcare system. Health inequities are not solely related to access to health care services; there are many other determinants related to living and working conditions, as well as the overall macro-policies prevailing in a country. The key intention of this review was to critically analyse the degree to which social determinants have impacted on excess alcohol consumption. A comprehensive approach to reduce inequities in alcohol-related harm requires action that includes mix of long- and short-term impacts, addressing the consequences and the root causes of inequities, and acting on both individuals and environments. Whereas, consequences of harmful alcohol use are more severe for those already experiencing social exclusion. We suggest that (1) the effective legislation, (2) modifying marketing strategies, (3) enhancing cooperation with regional organizations, (4) more effectively implementing existing regulation and (5) consulting expert will enhance SDH for this vulnerable population.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.020
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.276
GPT teacher head0.486
Teacher spread0.210 · 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

Labeled directly by 2 models reading the full record.

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

Citations10
Published2022
Admission routes1
Has abstractyes

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