Therapeutic alliance, patient behaviour and dropout in a drug rehabilitation programme: the moderating effect of clinical subpopulations
Bibliographic record
Abstract
AIM: Treatment dropout is an important concern for professionals working in mental health. While this problem is common, the highest attrition rates have been observed in drug rehabilitation programmes. The present study focuses on the therapeutic alliance, a process variable that has been associated repeatedly with positive treatment outcome in the scientific literature. Respondent behaviour indicative of commitment or resistance to treatment was examined in combination with therapist prognoses. DESIGN: A total of 248 subjects, classified into three subpopulations (justice, n = 50; mental health, n = 53; comparison group, n = 145), participated in the study. Analyses aimed at predicting dropout were conducted using Cox proportional-hazards regressions. The moderating effect of sub-population was tested. MEASUREMENTS: Respondents completed a multi-dimensional measure of alliance [California Psychotherapeutic Alliance Scale (CALPAS-P)]. Therapists rated the behaviour of respondents in treatment and made prognoses about perseverance and improvement. FINDINGS: An increased risk of dropout was predicted when patients viewed themselves as less committed and perceived the therapist as less understanding and less involved. Therapist prognosis of perseverance was also predictive of dropout. The relationship between patient/therapist evaluations and dropout is affected differently across subpopulations by means of a moderation effect. CONCLUSION: This paper demonstrates the capacity to predict dropout by measuring therapeutic alliance, therapist prognoses and therapist appraisal of patient behaviour. Moreover, the moderation effect of clinical subpopulation on treatment process variables and dropout is supported in the context of drug rehabilitation programmes.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".