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Record W2516824338 · doi:10.1080/09687637.2016.1216947

The geographic scope of opiate substitution therapy in an urban area in Canada

2016· article· en· W2516824338 on OpenAlexaffabout
Abe Oudshoorn, Ken Kirkwood

Bibliographic record

VenueDrugs Education Prevention and Policy · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsWestern University
Fundersnot available
KeywordsScope (computer science)Substitution (logic)OpiateGeographic variationSubstitution therapyGeographyMedicineEnvironmental healthComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

Opiate substitution therapy (OST) is an interdisciplinary treatment method for individuals experiencing opiate addictions. Municipalities internationally are working through a process of responding to both the need for OST clinics and community concerns around these clinics. The purpose of this quantitative descriptive study was to better understand the geographic spread of those currently accessing OST in an urban area in Canada. This will serve to assist related policy-making. Postal codes of 796 individuals accessing OST were obtained from one clinic and one dispensing pharmacy. Representing 581 unique data points, these were mapped across the 26 residential neighbourhoods in the city of study. Individuals accessing OST were located within an 11 km radius of the clinic and pharmacy. Situated in every neighbourhood in this radius, individuals accessing OST were in 24 of the 26 possible residential neighbourhoods. Ultimately, data support the hypothesis that individuals accessing OST are located in all residential neighbourhoods in the urban area of study. This supports current literature indicating that addiction exists throughout all urban areas rather than being limited to only certain neighbourhoods. This has implications for zoning of OST clinics and pharmacies, as municipalities must balance neighbourhood concerns while not overly restricting access throughout the municipality.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.611

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 head0.027
GPT teacher head0.349
Teacher spread0.322 · 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
Published2016
Admission routes2
Has abstractyes

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