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Record W4293150055 · doi:10.15288/jsad.22-00036

Contingency Management in Canadian Addiction Treatment: Provider Attitudes and Use

2022· article· en· W4293150055 on OpenAlexafffundabout
Megan Cowie, David C. Hodgins

Bibliographic record

VenueJournal of Studies on Alcohol and Drugs · 2022
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsContingency managementAddictionSuicide preventionContingency planPoison controlHuman factors and ergonomicsContingencyInjury preventionMedical emergencyMedicineOccupational safety and healthMEDLINEPsychologyForensic engineeringPsychiatryComputer securityEngineeringComputer sciencePolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: Contingency management (CM) is an evidence-based treatment for addictive disorders that is underused in clinical practice. The attitudes of staff are frequently reported as barriers to the uptake and use of evidence-based treatments, including CM. Understanding these barriers is an important step in implementation and could have an impact on an intervention's efficacy. We investigated Canadian addiction treatment providers' (ATPs) beliefs and use of CM. METHOD: = 298 respondents; female = 210, male = 80, other = 8) who offered services to help clients reduce substance use in their program(s). RESULTS: Providers in 103 programs across all 10 Canadian provinces participated (26.2% response rate). Most were not familiar with CM and reported largely neutral attitudes toward it. Training-related barriers to CM were the most highly endorsed compared with other barriers. Most ATPs reported a desire for additional training in CM. Some denied wanting additional training because of concerns about CM, which was consistent with previous literature. CONCLUSIONS: Our findings suggest that successful implementation of evidence-based treatments requires consideration of provider-level characteristics including attitudes, knowledge, and concerns about the intervention. Results highlight the importance of integrating training with efforts to address systemic-level barriers to implementation.

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 score0.025
Threshold uncertainty score0.625

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 head0.045
GPT teacher head0.321
Teacher spread0.276 · 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

Citations7
Published2022
Admission routes3
Has abstractyes

Explore more

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