Comparing three coaching approaches in pediatric rehabilitation : contexts, mechanisms and outcomes
Bibliographic record
Abstract
Objectives • Contrast unpinning theory and key processes of “Coping with and Caring for Infants with special Needs” (COPCA), “Occupational Performance Coaching” (OPC) and “Solution-Focused Coaching in Pediatric Rehabilitation“ (SFC-peds), which are coaching approaches used in family-centered pediatric rehabilitation • Discuss the evidence for key outcomes of coaching in relation to the goal achievement, engagement and capacity building of children, parents and families • Explore common misperceptions of coaching in relation to pediatric family-centered interventions and practices • Provide key messages regarding effective coaching approaches Summary: Coaching is en vogue in pediatric rehabilitation. However, coaching is not a single uniform method: different approaches with different assumptions exist and the role of the coach is interpreted in variable ways. Research on three conceptually distinct and practically grounded approaches, namely OPC, SFC-peds and COPCA, indicates that coaching can be a valuable type of intervention leading to empowerment and capacity building in families. Outline of the symposium • Schirin Akhbari Ziegler (PT, Switzerland): COPCA; theoretical background and translation into practice (15 minutes) • Fiona Graham (OT, New Zealand): OPC; approach to goal setting, Collaborative Performance Analyzes (15 minutes) • Gillian King (Canada): SFC-peds; conceptual background, key features and summary of evidence (15 minutes) • Schirin Akhbari Ziegler (Moderator): Facilitated discussion and close (45 minutes)
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".