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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".