Web-based early intervention for children with motor difficulties aged 3–8 years old using multimodal rehabilitation (WECARE): protocol of a patient-centred pragmatic randomised trial of paediatric telerehabilitation to support families
Bibliographic record
Abstract
Introduction Mild motor difficulties in children are underdiagnosed despite being highly prevalent, leaving such children often underserved and at higher risk for secondary consequences such as cardiovascular disease and anxiety. Evidence suggests that early patient-oriented interventions, coaching parents and providing children with early stimulation should be provided, even in the absence of a diagnosis. Such interventions may be effectively delivered via telerehabilitation. Methods and analysis A family-centred, pragmatic randomised controlled trial will be carried out to evaluate the real-world effectiveness of a Web-based Early intervention for Children using multimodAl REhabilitation (WECARE). Families of children with motor difficulties, 3–8 years of age, living in Quebec, Canada, and receiving no public rehabilitation services (n=118) will be asked to determine up to 12 performance goals, evaluated using the Canadian Occupational Performance Measure (COPM, the primary outcome). Families will be randomised to receive either usual care or the WECARE intervention. The WECARE intervention will be delivered for 1 year via a web-based platform. Families will have access to videoconferences with an assigned rehabilitation therapist using a collaborative coaching approach, a private chat function, a forum open to all intervention arm participants and online resources pertaining to child development. Participants will be asked to re-evaluate the child’s COPM performance goals every 3 months up to 1 year post allocation. The COPM results will be analysed using a mixed Poisson regression model. Secondary outcomes include measures of the child’s functional ability, parental knowledge and skills and health-related quality of life, as well as qualitative outcomes pertaining to parental satisfaction and service delivery trajectories. Investigators and quantitative data analysts will be blinded to group allocation. Ethics and dissemination The CIUSSS de l’Estrie—CHUS ethics committee approved this trial (2020-3429). Study results will be communicated via peer-reviewed journal publications, conference presentations and stakeholder-specific knowledge transfer activities. Trial registration number NCT04254302 .
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.045 | 0.006 |
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".