Co-designing solutions to enhance access and engagement in pediatric telerehabilitation
Bibliographic record
Abstract
Introduction: Prior to the COVID-19 pandemic, children's therapy appointments provided by Ontario's publicly-funded Children's Treatment Centre (CTCs) primarily occurred in-person. With COVID-19 restrictions, CTCs offered services via telerehabilitation (e.g., video, phone), which remains a part of service delivery. CTC data shows that families experience barriers in attending telerehabilitation appointments and may need supports in place to ensure service accessibility. Our study aimed to co-design innovative solutions to enhance access and engagement in ambulatory pediatric telerehabilitation services. This manuscript reports the co-design process and findings related to solution development. Methods: This research project used an experience based co-design (EBCD) approach, where caregivers, clinicians and CTC management worked together to improve experience with telerehabilitation services. Interview data were collected from 27 caregivers and 27 clinicians to gain an in-depth understanding of their barriers and successes with telerehabilitation. Next, 4 interactive co-design meetings were held with caregivers, clinicians and CTC management to address priorities identified during the interviews. Using qualitative content analysis, data from the interviews and co-design meetings were analyzed and findings related to the solutions developed are presented. Findings: Four topics were identified from the interview data that were selected as focii for the co-design meetings. Findings from the co-design meetings emphasized the importance of communication, consistency and connection (the 3C's) in experiences with telerehabilitation. The 3C's are represented in the co-designed solutions aimed at changing organizational processes and generating tools and resources for telerehabilitation services. Discussion: The 3C's influence experiences with telerehabilitation services. By enhancing the experience with telerehabilitation, families will encounter fewer barriers to accessing and engaging in this service delivery model.
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".