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Record W1459909372 · doi:10.1007/978-88-470-5322-9

Textbook of Addiction Treatment: International Perspectives

2015· book· en· W1459909372 on OpenAlexaff
Nady el‐Guebaly, Giuseppe Carrà, Marc Galanter

Bibliographic record

Venuenot available
Typebook
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsAlberta Gambling Research InstituteUniversity of Calgary
Fundersnot available
KeywordsAddictionPsychologyNeuroscience

Abstract

fetched live from OpenAlex

Substance use and abuse is widespread around the world. There are clear cultural differences between countries and sometimes even within the same country regarding the use and abuse of substances. Various cultural and social factors play an important role in defining and treating patterns of substance abuse. Reasons for substance use and abuse vary from genetic, social, and psychological. Depending upon the degree of abuse and recognition of the patterns of abuse, help may be sought from a number of sources. Cultures and societies dictate how resources are allocated and how people are encouraged to use certain pathways into care. Identifying the unmet needs and mental health gap in the field of substance use and abuse is the first crucial step.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.053
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0530.031

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 head0.019
GPT teacher head0.291
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations146
Published2015
Admission routes1
Has abstractyes

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