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Record W1987422221 · doi:10.1002/mpr.151

Screening for antisocial personality disorder in drug users – a qualitative exploratory study on feasibility

2003· article· en· W1987422221 on OpenAlexafffund
Benedikt Fischer, Emma Haydon, Gregory Kim, Jürgen Rehm, Nady el‐Guebaly

Bibliographic record

VenueInternational Journal of Methods in Psychiatric Research · 2003
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsFoothills Medical CentreUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsPsychologyAddictionAntisocial personality disorderPersonalityQualitative researchClinical psychologyPerspective (graphical)PopulationPersonality disordersPsychiatryMethadoneExploratory researchBorderline personality disorderMethadone maintenancePsychotherapistMedicinePoison controlInjury preventionSocial psychologyMedical emergency

Abstract

fetched live from OpenAlex

Knowledge about co-occurring personality disorders in drug users is important for planning therapy and prevention. The objective of this study was to assess whether the SCID-II (Structured Clinical Interview for DSM-III-R) Screen for antisocial personality disorder was feasible and acceptable in a population of opioid users. A qualitative study on veridicality and emotional quality in responses to SCID-II Screen was carried out by personal interview in a multifunctional addiction centre. The subjects were 10 outpatient participants (six female, four male) in methadone substitution treatment. The SCID-II Screen triggered a high level of emotions. Some questions were mainly interpreted from a victim's perspective, even though the intention was the perpetrator's view. Questions were seen as sex-biased. Provision of support to deal with potential emotional problems should be supplied. Potential revision should be considered to include the female perspective in the screen.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.022
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.397
GPT teacher head0.633
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2003
Admission routes2
Has abstractyes

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Same venueInternational Journal of Methods in Psychiatric ResearchSame topicSubstance Abuse Treatment and OutcomesFrench-language works237,207