Replication of psychometric properties and predictive validity of the Important People Drug and Alcohol Interview
Bibliographic record
Abstract
Owens, M., & Zywiak, W. (2016). Replication of psychometric properties and predictive validity of the Important People Drug and Alcohol Interview. The International Journal Of Alcohol And Drug Research, 5(2), 57-64. doi:http://dx.doi.org/10.7895/ijadr.v5i2.217Aims: Social support is a predictor of alcohol and drug use. The Important People Drug and Alcohol (IPDA) interview and its predecessor, the Important People and Activities (IPA) measure, have been used to demonstrate this predictive relationship. The purpose of this study was to replicate the findings from Zywiak et al. (2009) in a sample of probationers with substance use disorders.Design: Analyses mirrored those done previously to replicate the associations between social networks and substance use. The IPDA was used to assess social networks before and after incarceration. Form-90 (Tonigan, Miller, & Brown, 1997) was used to measure substance use.Participants: Individuals were recruited from a local probation office. Information was collected from a sample of 50 male probationers with substance use disorders recently released from jail.Conclusions: Results showed that many of the previous findings from Zywiak et al. (2009) were similar to those found in the current study. This adds to the evidence that the IPDA is a promising measure of social networks and examining how those networks relate to substance use outcomes. The use of the IPDA may be beneficial for both research and clinical purposes, while evaluating individuals with alcohol and other drug use disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".