TelereHUB-CHILD: An online integrated knowledge translation tool to optimize telerehabilitation evidence-based practices for children with disabilities and their families
Bibliographic record
Abstract
Background Pediatric telerehabilitation has been quickly adopted by clinicians during the pandemic. This precipitated shift in the model of healthcare delivery is significant and compounded by clinicians' training and knowledge needs related to evidence-based practices. This instigated a knowledge translation initiative TelereHUB-CHILD—an online platform designed for clinicians, patients, and families. The aim of this brief report is to describe its development, including the roles of key stakeholders in these processes. Methods Following a systematic review on telerehabilitation, a series of co-creation activities with clinical (n = 24 rehabilitation professionals) and parent-partners (n = 4 parents of children with disabilities) were undertaken. Clinical partners were engaged in five web-activities. These were designed to gather their feedback regarding training and knowledge needs, present preliminary findings of the systematic review and explore their perceived importance and usefulness with respect to different sections of TelereHUB-CHILD, including Tele-treatments, Tele-Assessments, and Resources. Parent-partners were engaged asynchronously to provide feedback on the content and presentation of the Patient/Family Information section. Results Clinical partners reported moderate-high usefulness and importance with each section of the tool and the presented features. As per partners' feedback, the Tele-treatments section provides standardized summaries outlining the effectiveness of the tele-treatment approach and the level of the evidence for each outcome of interest, according to the different diagnosis groups and professional discipline. For patients/family, common questions and answers can be explored in three user-friendly formats, including printable learning briefs, onsite accordions, and animation videos. The Tele-assessments section outlines existing measures by professional discipline. Resources offer preparatory forms for families and clinicians, questionnaires, and other learning material. Conclusion TelereHUB-CHILD was co-developed with key stakeholders. It can guide telerehabilitation evidence-based practices, empower patients and families, and pinpoint research and practice gaps.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".