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Record W2728757889 · doi:10.35502/jcswb.43

An innovative service collaboration to reduce criminal recidivism for inmates with severe addictions

2017· article· en· W2728757889 on OpenAlexaffvenueabout
Elan Paluck, Michelle McCarron, Mamata Pandey, Dorothy Banka

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsGovernment of SaskatchewanRegina Qu'Appelle Health Region
Fundersnot available
KeywordsGeneral partnershipChristian ministryPublic relationsSubstance abuseAddictionCompromiseService (business)StakeholderCriminal justiceRecidivismBusinessEconomic JusticeMedical educationPsychologyNursingPolitical scienceMedicinePsychiatryMarketingCriminology

Abstract

fetched live from OpenAlex

Agencies with overlapping mandates can form partnerships to aid development of effective programming. In 2008, the Dedicated Substance Abuse Treatment Unit (DSATU) opened at Regina Correctional Centre through a tripartite collaboration between the Saskatchewan (SK) Ministry of Justice, Corrections and Policing; Addiction Services, Regina Qu’Appelle Health Region; and the SK Ministry of Health. Stakeholders researched existing best practices in the field and developed an evidence-based substance abuse treatment program for sentenced inmates at high risk to re-offend. An evaluation of the DSATU program completed in 2016 concluded that the DSATU was effective, sustainable, and likely transferable to other correctional facilities wishing to offer this type of programming. The stakeholder partnership was a key ingredient in the program’s success. This paper describes the process by which the partners worked together to develop, implement, and sustain this innovative and evidence-based substance abuse treatment program. The partners’ willingness to compromise, to take a collaborative approach to building the partnership and developing the program, and to put clients ahead of individual organizational mandates all contributed to the success of the partnership.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.248
GPT teacher head0.572
Teacher spread0.324 · 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.

Study designQualitative
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
Published2017
Admission routes3
Has abstractyes

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