Interprofessional Clinical Supervision in Mental Health and Addiction: Toward Identifying Common Elements
Bibliographic record
Abstract
This study explores the experiences and perceptions of clinicians from a range of professions to articulate general principles for clinical supervision in mental health. Seventy-seven volunteer clinicians participated in 14 focus groups in 2008–2009. They discussed their perceptions about clinical supervision, facilitators, and barriers. Discussions were digitally recorded and transcribed verbatim, and qualitative analytic methods were used to identify themes and exceptions. The study found frontline clinicians identified interacting factors they associated with quality clinical supervision. Themes related to the structure, content, and process of supervision and contained common elements across professions and those that were specific to nursing. Considerable agreement exists regarding principles for interprofessional supervision in mental health; that it is available on a regular and crisis-responsive basis, and that supervisors are expert in clinical interventions for specific populations and have the skills for teaching and supporting staff. Some nurse participants expressed unique perceptions about clinical supervision based on their professional traditions and approaches, which requires further study before advancing a common model of supervision across professions.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".