Using an innovative model of service delivery to identify children who are struggling in school
Bibliographic record
Abstract
Introduction School-age children with motor coordination challenges typically require formal referral for occupational therapy services and often experience lengthy wait times for one-to-one intervention. In a new service delivery model called Partnering for Change, therapists work collaboratively with educators in classrooms to observe, identify, and support children. This study describes children identified through a traditional referral process and compares them with children identified by occupational therapists through classroom observation and dynamic performance analysis. Methods Participants included 246 children enrolled in a 2-year evaluative study of the Partnering for Change service delivery. Parents completed a demographic questionnaire, the Developmental Coordination Disorder Questionnaire, and the Strengths and Difficulties Questionnaire. Children’s educators completed the Strengths and Difficulties Questionnaire and the School Function Assessment. Children completed the Movement Assessment Battery for Children. Results Children identified were significantly younger and more likely to be girls than those referred under the traditional model. Using observation and dynamic performance analysis, occupational therapists identified children who had equally marked difficulties as those who came from the waitlist. In the Partnering for Change model, waitlists for service were eliminated for all children. Conclusions Occupational therapists can identify children who are experiencing significant challenges participating at school without the need for standardized assessment, formal referrals, and waitlists.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".