Factors Influencing Telehealth Use in School-based Health Services: Secondary Analysis from a Scoping Review
Bibliographic record
Abstract
Introduction: Although telehealth use in schools can address gaps in service access, implementation in the school setting lags. This study describes factors that influence implementation of telehealth in school health services. Methods: . Using scoping review methods, articles were sought in five academic databases pertaining to regulated health providers' use of telehealth in kindergarten to grade 12 schools. Two reviewers completed source selection and data extraction. Data were charted to the diffusion of innovations theory and content analysis performed. Results: Of 6585 unique sources considered, 70 articles were included. Multiple factors were described influencing telehealth implementation in schools. The most salient factors reported for successful implementation included provider training, access to reliable technology, availability of an e-helper, and policies to support ethical telehealth delivery. Conclusion: Telehealth use in schools is increasing; however, successful implementation requires planning that considers how and why such innovations are adopted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".