Balancing Challenges and Facilitating Factors when Implementing Client-Centred Collaboration in a Mental Health Setting
Bibliographic record
Abstract
This study undertook a replication of the work conducted by Sumsion in 2004 in the United Kingdom regarding the application of a definition of client-centred practice. Twelve occupational therapists employed by a local mental health facility and working with adult outpatients participated in semi-structured interviews. Template analysis and open coding were used to analyse the data. The resulting concept map indicated that collaboration and meaningful goals were at the centre of client-centred practice and formed the two main categories of data. The therapist and the client were the protagonists in these categories, but the family, team and system also played major roles. A table within this paper outlines all the categories and themes that arose from the data. However, space limitations required a focus on only the category of collaboration and the therapist and client facilitators and challenges within this category. The therapists used both attitudes and actions to facilitate the client-centred process and the clients brought strengths to this relationship. Nevertheless, both groups faced many challenges that had to be overcome to enable the successful implementation of client-centred practice.
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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 | 0.136 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".