Decisional Support Algorithm for Collaborative Care Planning using the Participation and Environment Measure for Children and Youth (PEM-CY): A Mixed Methods Study
Bibliographic record
Abstract
ABSTRACT Aims: The purpose of this study was to explore the utility of the Participation and Environment Measure for Children and Youth (PEM-CY) for collaborative care planning with parents of children with disabilities. Methods: An explanatory sequential mixed methods approach was employed to examine how community-based service providers interpret and apply PEM-CY case results to set goals and formulate care plans with parents. We used two distinct, interactive phases that included collection and summary of PEM-CY data in Phase One (quantitative) and sequential collection and analysis of interview data during Phase Two (qualitative). Twenty-three parents of children with disabilities (mean age = 10.7 years) completed the PEM-CY community section during Phase One (quan). Four PEM-CY case reports were used with seven providers who were interviewed during Phase Two (QUAL). Results: Providers identified a four-step decisional support algorithm for leveraging PEM-CY case results in care planning: (1) parent rank orders activities in which change is desired, (2) child preferences are incorporated, (3) provider clarifies parent and child goals, and (4) activity-specific supports, barriers, and strategies are identified. Conclusions: Further validation and refinement of the decisional support algorithm with parents and children when applied to PEM-CY home and school reports is discussed.
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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 | 0.067 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".