Using the capability, opportunity, and motivation model of behaviour to assess provider perception of implementing solution-focused goal-setting in paediatric rehabilitation
Bibliographic record
Abstract
Adoption of family and child goal-setting in paediatric rehabilitation is important to positive long-term outcomes. Solution-focused coaching (SFC) has been identified as a promising approach to ensuring this type of goal-setting occurs, while the actual implementation of SFC by health care providers (HCPs) is low. This study utilized the capacity, opportunity, and motivation model of behaviour change (COM-B) to identify which strengths and difficulties health care providers (HCPs) perceived with respect to SFC goal-setting in paediatric rehabilitation. A self-report survey was developed and administered to HCPs at a paediatric rehabilitation hospital. Each survey question was based upon a COM-B sub-component. Demographic information was collected from HCPs, and descriptive statistics were used to rank perceived COM-B components from strongest to weakest. Results indicate HCPs view the provision of SFC goal-setting as an important practice, while they also perceive difficulties to actual delivery due to: lack of adequate individual skill, lack of experience with this type of goal-setting, and insufficient preparation for clients to engage in sharing their goals. HCPs also perceived lack of organizational processes to support the practice within their teams. Recommendations for intervention are provided.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".