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Record W4323256817 · doi:10.1080/27707571.2023.2177132

Alcohol Use disorders and harm-reduction in indigenous coastal communities of Hudson Bay Northern Canada

2023· article· en· W4323256817 on OpenAlexaboutno aff
Arnold Hill, Patricia Benson, Richard Hill

Bibliographic record

VenueCogent Public Health · 2023
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousBinge drinkingPsychosocialPopulationEnvironmental healthHarm reductionMedicinePoison controlSuicide preventionIntervention (counseling)GeographyPsychiatryPublic healthEcology

Abstract

fetched live from OpenAlex

: Alcohol use disorders are a major contributor to morbidity and mortality across the globe. Binge drinking and suicide have a high prevalence in northern latitudes, including Canada, Alaska, Greenland, and northern Europe. Many factors are associated with alcohol misuse and suicide in these northern regions such as climate, geographic location, history of the population, which in Canada includes colonisation-related harms such as the introduction of alcohol and systematic suppression of native cultures, on-going psychosocial stressors, and governmental policies. Due to the high prevalence of alcohol misuse and suicide in the studied population, the authors introduced contextually relevant Brief Intervention (BI) into the ER and ambulatory care settings as a harm-reduction measure. The impact of BI on binge drinking and suicide is discussed. The co-ordination of BI with other harm-reduction strategies in the region include suicide awareness and intervention training, efforts by provincial and federal governmental agencies using special teams to limit the social and psychological impact of recent suicides in communities, the return of AA groups after a ten-year hiatus, and importantly the establishment of native healing programmes.

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)0.000
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 score0.462
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.075
GPT teacher head0.309
Teacher spread0.234 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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