Working alliance patterns in a context of supported employment programmes for people with a severe mental illness: An employment specialist perspective
Bibliographic record
Abstract
Background: Developing a good working alliance with clients with a severe mental illness (SMI) is a core competency of the employment specialist (ES). The ES's assessment of the working alliance was found to be related to the client's acquisition of a job in the regular market but we have little information on the processes and factors involved. Objective: To understand the development of the work alliance as assessed by the ES and its relationship to the client's acquisition of employment. Factors that may facilitate or hinder the development and evolution of the alliance were also explored. Methods: Cluster analysis was used to define alliance development patterns, while frequency analyses were used to identify differences between the patterns in terms of whether the clients with SMI obtained (or not) employment. Interviews with ESs explored factors that may have explained the different patterns. Results: Three patterns of working alliance were found and the one most often linked to client employment was the very high and stable pattern. The factors that might explain the different patterns are complex and interrelated. Conclusion: The results can be considered in the ES's initial and ongoing training on the working alliance and the implementation of quality supported employment 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".