The use of the cognitive-behavioral relapse model in understanding individuals with relapse in inhalant abuse, a preliminary study
Bibliographic record
Abstract
The purpose of this preliminary study was to explore the usefulness of Marlatt's Cognitive-Behavioral Relapse model in understanding inhalant abuse relapse. Understanding the nature of relapse through an existing model is fundamental to exploring and understanding inhalant abuse relapse. The present qualitative study consisted of two in depth interviews, one with an Aboriginal adult female and the other with an Aboriginal adult male. Both are recovering inhalant abusers who had received treatment, although relapsed the day they returned to their community. Analysis of the reports suggest these two individuals report similar relapse factors to those described in the literature by Marlatt's Cognitive-Behavioral Relapse Model (e.g., low perceived self-control/self-efficacy, ineffective cognitive and behavioral responses in the high-risk situation, attending to the positive outcome expectancies for the initial effects of the substance, dissonance conflict and self-attribution). However, the validity of one participant's results may have been compromised due to social desirability factors, lack of rapport with the examiner, and the difficulty in expressing one's true feelings. As the relapse factors are similar, it seems that Marlatt's model could be useful in understanding and explaining inhalant abuse relapse. Both interviewees felt they needed more support in aftercare and stressed that this be "quality" support in addition, very strong themes of "personal challenges" (e.g., peer pressure to sniff inhalants, extreme difficulty in quitting-sniff ) were found.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".