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Record W1759121286 · doi:10.1080/10550887.2015.1059651

Harm Reduction: Front Line Public Health

2015· article· en· W1759121286 on OpenAlexaff
Sharon Stancliff, Benjamin Phillips, Nazlee Maghsoudi, Herman Joseph

Bibliographic record

VenueJournal of Addictive Diseases · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of TorontoGlobal Affairs Canada
Fundersnot available
KeywordsHarm reductionPublic healthAbstinenceMedicineHarmFront lineGeneral partnershipHealth careEnvironmental healthPsychiatryNursingPsychologyPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Drug use is a public health problem associated with high mortality and morbidity, and is often accompanied by suboptimal engagement in health care. Harm reduction is a pragmatic public health approach encompassing all goals of public health: improving health, social well-being, and quality of life. Harm reduction prioritizes improving the lives of people who use drugs in partnership with those served without a narrow focus on abstinence from drugs. Evidence has shown that harm reduction oriented practice can reduce transmission of blood-borne illnesses, and other injection related infections, as well as preventing fatal overdose.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.121
GPT teacher head0.392
Teacher spread0.271 · 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 designNot applicable
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

Citations49
Published2015
Admission routes1
Has abstractyes

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