Can asking people on probation about their strengths improve affect, alliance, engagement, motivation and prosocial identity?
Bibliographic record
Abstract
Forensic psychology has historically focused on assessing and managing deficits that contribute to offending. Recently, there has been a push across fields towards focusing on strengths in both assessment and treatment, which may have therapeutic benefits for clients (e.g. emotional well-being, positive identity, therapeutic alliance). As this has yet to be tested, we conducted a pre-registered experiment to examine the effects of strength- and risk-focused interviews on a number of therapeutic process variables. A sample of individuals on probation were randomly assigned to have a strength-focused ( n = 51) or risk-focused interview ( n = 50). Immediately after and 1 month later, participants completed measures of affect, alliance, engagement, motivation to change and identity. Contrary to our pre-registered hypotheses, participants who had a strength-focused interview did not report more positive affect ( p = .46, η 2 [95% CI] = .027 [.000, .077]), a stronger alliance with the interviewer ( p = .92, η 2 [95% CI] = .005 [.000, .051]), more engagement ( p = .58, η 2 [95% CI] = .020 [.000, .071]), or greater readiness to change ( p = .19, η 2 [95% CI] = .061 [.000, .092]) than those having a risk-focused interview immediately or 1 month later. However, participants who had a strength-focused interview perceived themselves as less likely to reoffend in the future ( p = .02, η 2 [95% CI] = .051 [.000, .154]), which remained significant 1 month later when we controlled for interviewer adherence to condition ( p = .04, η 2 [95% CI] = .052 [.000, .17]). We offer minimal initial support for the unique benefits of strength-focused (vs. risk-focused) interviews. Future research should use larger samples with adequate power to detect small effects and examine if strength-focused interviews are beneficial for culturally and linguistically diverse groups.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".