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Record W4366588781 · doi:10.1186/s12954-023-00776-z

Safer opioid supply: qualitative program evaluation

2023· article· en· W4366588781 on OpenAlexafffundabout
Marlene Haines, Patrick O’Byrne

Bibliographic record

VenueHarm Reduction Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of Ottawa
FundersHealth Canada
KeywordsSAFERHealth psychologyOpioidQualitative researchPublic healthBusinessMedicineNursingComputer securitySociologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: As the overdose crisis in Canada continues to escalate in severity, novel interventions and programs are required. Safer Supply programs offer pharmaceutical-grade medication to people who use drugs to replace and decrease harms related to the toxic illicit drug supply. Given the paucity of research surrounding these programs, we sought to better understand the experience of being part of a Safer Supply program from the perspective of current participants. METHODS: We completed semi-structured interviews and surveys with Safer Supply participants in Ottawa, Canada. Interviews were audio-recorded, transcribed, and analyzed thematically. Descriptive statistics were used to report survey data. RESULTS: Participants most commonly discussed Safer Supply benefits. This included programs offering a sense of community, connection, hope for the future, and increased autonomy. Participants also described program concerns, such as restrictive protocols, inadequate drugs, and diversion. CONCLUSIONS: Our research demonstrated that participants found Safer Supply to be effective and impactful for their substance use goals. While participants did discuss concerns about the program, overall, we found that this is an important harm reduction-based program for people who use drugs in the midst of the overdose crisis.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.084
GPT teacher head0.440
Teacher spread0.356 · 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

Citations42
Published2023
Admission routes3
Has abstractyes

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