Working Alliance and Its Relationship With Treatment Outcome in a Sample of Aboriginal and Non-Aboriginal Sexual Offenders
Bibliographic record
Abstract
The relationship that develops between a client and therapist is arguably one of the most important factors toward achieving positive outcomes from therapy. The present study examined the therapeutic alliance, as measured by Horvath and Greenberg's Working Alliance Inventory (WAI), as a function of Aboriginal ancestry and the relationship of alliance to important program outcomes, in a Canadian correctional sample of 423 treated sexual offenders. The men rated their primary therapists on the WAI 3 months into treatment. Higher self-report ratings on the WAI and its Task, Bond, and Goal subscales were associated with lower rates of treatment non-completion and longer stay in treatment. Aboriginal men scored significantly lower on the WAI's Bond subscale (i.e., the emotional connection between client and therapist) than non-Aboriginal men, although by and large, the offender sample as a whole otherwise registered fairly high mean scores on the tool. Aboriginal men scoring below the median on WAI total score had the highest rates of treatment non-completion. WAI total score and scores on the three subscales were unrelated to post-program recidivism in the community. Cultural implications for correctional client engagement and service delivery within the context of the risk-needs-responsivity model are discussed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".