Acceptance and Commitment Therapy-Based Intervention to Manage Stigma toward Substance use Disorders
Bibliographic record
Abstract
Abstract Objective Stigma of health service professionals toward people with substance use disorders seriously compromises quality of care. A worldwide agenda points out how crucial it is to invest in evidence-based interventions to address stigma. This study aimed to describe the development process of an innovative intervention protocol to manage stigma based on Acceptance and Commitment Therapy. Method This is an empirical qualitative study. Phase 1: Guiding axes from evidence-based literature to define the active components. Phase 2: Protocol preliminary version and reporting based Template for Intervention Description and Replication guide. Phase 3: Expert judges to assess the first step of the content validation. Results We present 10 active components we achieved through a 12-hour face-to-face intervention, evaluated by expert judges. Conclusion This intervention is notable in terms of its coverage, accuracy, and suitability for health service professionals, and seems promising to be implemented for testing and replication.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".