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Record W4317402231 · doi:10.1186/s12954-023-00733-w

Attitudes towards people who use substances: a survey of mental health clinicians from an urban hospital in British Columbia

2023· article· en· W4317402231 on OpenAlexafffundabout
Angela Russolillo, Meijiao Guan, Elizabeth J. Dogherty, Maja Kolar, Jennifer Runhong Du, Elísabet Brynjarsdóttir, Michelle Carter

Bibliographic record

VenueHarm Reduction Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsCentre for Advancing Health OutcomesSt. Paul's HospitalSimon Fraser UniversityProvidence Health Care
FundersProvidence Health Care
KeywordsMental healthHarm reductionMedicineSubstance abuseHealth psychologyPsychiatryOptimismClinical psychologyStigma (botany)Family medicinePublic healthPsychologyNursingSocial psychology

Abstract

fetched live from OpenAlex

Stigma and other barriers limit harm reduction practice integration by clinicians within acute psychiatric settings. The objective of our study was to explore mental health clinician attitudes towards substance use and associations with clinical experience and education level. The Brief Substance Abuse Attitudes Survey was completed among a convenience sample of mental health clinicians in Vancouver, British Columbia. Five predefined attitude subgroups were evaluated. Respondents' attitudes towards substance use were associated with level of education on questions from two (non-stereotyping [p = 0.012] and treatment optimism [p = 0.008]) subscales. In pairwise comparisons, postgraduate education was associated with more positive attitudes towards relapse risk (p = 0.004) when compared to diploma-educated respondents. No significant associations were observed between years of clinical experience and participant responses. Our findings highlight important aspects of clinician attitudes that could improve harm reduction education and integration into clinical practice.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.040
GPT teacher head0.338
Teacher spread0.298 · 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

Citations8
Published2023
Admission routes3
Has abstractyes

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