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Record W3194368778 · doi:10.22605/rrh6413

Pilot program integrating outpatient opioid treatment within a rural primary care setting

2021· article· en· W3194368778 on OpenAlexaffabout
Buck-McFadyen, Lee-Popham

Bibliographic record

VenueRural and Remote Health · 2021
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsTrent University
Fundersnot available
KeywordsMedicineContext (archaeology)BuprenorphineNursingAddictionRural areaOpioid use disorderHarm reductionPublic healthFamily medicinePsychiatryOpioid

Abstract

fetched live from OpenAlex

CONTEXT: Canada is experiencing an opioid crisis. In rural areas, limited access to specialty addictions services, public transportation, and many of the social determinants of health create a unique set of challenges for people who use substances. ISSUE: The Rural Outpatient Opioid Treatment (ROOT) program was created to bring some of the structure of an inpatient treatment program into a rural primary care setting in Ontario, Canada. The program uses a harm reduction approach to provide group recovery work, primary care, peer support, smoking cessation, opioid agonist therapy, screening and treatment for hepatitis C and HIV, and longitudinal follow-up. Sixteen participants have enrolled in three rounds of the ROOT program to date. LESSONS LEARNED: A program evaluation shows that opioid use decreased while use of other substances remained high, in particular methamphetamine use, which is increasing more broadly in the local area. Participants described feeling cared for and appreciated the 'seamless' nature of the multidisciplinary program, the peer support provided, and their new and expanded social networks. The rural context created both benefits and challenges for their substance use, recovery, and for community programming. In conclusion, the evaluation of this pilot program demonstrates that it is possible to successfully integrate an outpatient substance-use treatment program into rural primary care.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.801

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 head0.016
GPT teacher head0.301
Teacher spread0.286 · 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 designOther design
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

Citations0
Published2021
Admission routes2
Has abstractyes

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