“Another Tool in Your Toolkit”: Pediatric Occupational and Physical Therapists’ Perspectives of Initiating Telehealth during the COVID-19 Pandemic
Bibliographic record
Abstract
Pediatric occupational and physical therapy service delivery via telehealth increased during the COVID-19 pandemic. Real-world experience can guide service improvement. This study explored experiences, barriers, and facilitators of initial telehealth implementation from the therapist’s perspective. Qualitative descriptive approach. Semi-structured interviews were conducted with occupational therapists (n = 4) and physical therapists (n = 4) between May-June 2020. Interviews were recorded, and transcribed verbatim. Data were coded inductively to generate themes, then re-coded deductively to classify barriers and facilitators to telehealth acceptance and use using the Unified Technology Acceptance Theory. Participants had 16.5 [(2-35); median (range)] years of experience (3 months with telehealth) and predominantly worked with preschool children. Three themes about telehealth were identified: a practical option; requires skill development and refinement; beneficial in perpetuity. Most frequently cited barriers were the lack of opportunity for ‘hands-on’ assessment/intervention and the learning curve required. Most frequently cited facilitators included seeing a child in their own environment, attendance may be easier for some families, and families’ perception that telehealth was useful. Despite rapid implementation, therapists largely described telehealth as a positive experience. Telehealth facilitated continued service provision and was perceived as relevant post-pandemic. Additional training and ensuring equitable access to services are priorities as telehealth delivery evolves.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".