Evaluating the Feasibility of a Clinical Supervision Model for Evidence-supported Interventions for Children with Severe Disruptive Behaviour
Bibliographic record
Abstract
The purpose of this dissertation was to enhance knowledge of the practice and role of staff supervision in supporting children’s mental health services and the implementation of evidence-supported interventions. This research examined the need, acceptability, and feasibility of implementing a model of clinical supervision to support the SNAP (Stop Now and Plan) evidence-supported interventions for children with disruptive behaviours and their families. Methods: In Paper 1, a descriptive cross-sectional survey of supervisors and practitioners (N = 54) established supervision in practice within Canadian SNAP evidence-supported interventions. In Paper 2, a multiple case study evaluated the acceptability, appropriateness, adoption, and feasibility of implementing an intervention-specific model of clinical supervision within SNAP sites (N = 3). In Paper 3, a feasibility study of implementing the SNAP model of clinical supervision in community sites (k = 6) over a six-month period examined the demand, acceptability, implementation, practicality, and effectiveness of the model on enhancing practitioner competence, and perceived support (N = 27). Results: Paper 1 showed that supervision was valued, however occurring variably across the SNAP affiliate sites. The results highlighted an opportunity to enhance the quality of supervision within the SNAP affiliate sites. Paper 2 demonstrated that the SNAP clinical supervision model could be adopted in practice with adjustments, was deemed acceptable and appropriate by SNAP supervisors, and was predominately feasible with real world circumstances taken into consideration. Paper 3 findings included a determination of partial demand for implementing the SNAP clinical supervision model, as well as the acceptability of the model, implementation benefits and challenges, challenges related to the practicality of implementing the model using existing resources, and the preliminary effectiveness of the model on enhancing practitioner competence. Conclusions: This dissertation contributes an understanding of the need for a structured model of supervision, as well as demonstrates how such a model incorporating theory and research can be specified and translated to practice. The studies demonstrate the feasibility of implementing the SNAP CS in community-based sites. The dissertation employed a systematic approach to researching supervision to present the first known Canadian study of implementing a model of clinical supervision in 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 | 0.077 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".