Make me why don't you: understanding barriers to treatment engagement in coercive contexts
Bibliographic record
Abstract
The high cost of mandated or coercive treatment in terms of time, money, and emotional distress highlights the importance of determining whether and how this kind of treatment can lead to positive outcomes. Findings suggest that even treatment resistant patients can benefit given the right circumstances. A recent Task Force concluded that there was evidence for the influence of three factors in treatment outcomes including relationship principles, non-diagnostic patient characteristics and the technical details of the treatment. The authors called for future research to further their stated aim, “to identify empirically based principles of change in psychotherapy …that provide guidelines about how to most effectively deal with clients that aren’t tied to particular approaches or theories” (Castongauy & Beutler, 2006, p. 632).The aim of the present study was to examine factors thought to influence relationship principles (patient perceptions of the hospital admission process and subsequent treatment) and patient characteristics associated with negative treatment outcomes (antisocial personality traits and negative emotionality) in an attempt to identify which variables are at play during the experience of treatment that prevent active participation (therapeutic alliance, treatment motivation and treatment compliance). The participants were 139 civil psychiatric patients recently discharged. Data was collected via semi-structured interview and record review at baseline and 5 prospective follow-ups to examine relationships between variables over time. Results indicated that patient perceptions are related to treatment indices at baseline and these relationships are stable over time. Further, antisocial personality traits were related to treatment compliance and dispositional anger. Findings hold implications for the impact of interventions designed to target treatment interfering perceptions and emotions at initial contact, on treatment engagement over time.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".