Quest for client autonomy in improving long‐term mental health care
Bibliographic record
Abstract
The objective of the present study was to explore how mental health-care professionals initiate, improve, and maintain client autonomy while improving other aspects of quality of care. We studied the different ways in which they approach autonomy and the dilemmas associated with them. As a methodology, we used the insights of actor-network theory, where concepts cannot be predefined, but are formed within specific situations, and therefore, should be studied by addressing the actors involved. Data were gathered by conducting ethnographic observations of national conferences of a quality-improvement collaborative and by interviewing actors involved in the improvement practices. In a bottom-up analysis, four approaches to autonomy emerged: (i) professionals removed constraints to autonomy and passed initiative to clients; (ii) professionals made an active effort to learn and support client preferences; (iii) clients were given opportunities towards independent lifestyles; and (iv) professionals tried to 'normalize' their relationship with clients to encourage roles other than those of client. The study showed that autonomy is an important issue throughout the process of quality improvement. Articulating the different approaches to autonomy and the dilemmas in these approaches contributed to reflection on the concept and highlighted the limits of the concept within a mental health-care setting.
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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 | 0.032 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 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".