Pediatric Teleheath: Opportunities Created by the COVID-19 and Suggestions to Sustain Its Use to Support Families of Children with Disabilities
Bibliographic record
Abstract
AIMS: Telehealth is being rapidly adopted by physical and occupational therapists in pediatrics as a strategy to maintain services during the COVID-19 crisis. This perspective presents a mix of theoretical and practice perspectives to support the implementation of telehealth. Although research evidence is just emerging, there is sufficient indication to believe telehealth is effective. However, which telehealth strategies are best for which children and families, and which intervention goals, are not yet clear. METHODS: We discuss how different telehealth strategies (e.g. videoconferencing, emails, phone calls, online programs) are being used to address specific intervention goals. Comments from therapists using telehealth and examples of practices in different context and with different populations are provided. We discuss how newly adopted telehealth practices could be included in future hybrid service delivery models and programs, as well as factors influencing the decision to offer face-to-face or online interventions. CONCLUSION: Although telehealth has been implemented quickly as a response to a health care crisis, and is not a one-size-fits-all intervention, we believe it offers great opportunities to increase the accessibility, cost-effectiveness and family-centredness of our services, to best support families of children with disabilities.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".