A scoping review of telehealth in school-based health services: Characteristics of telehealth use
Bibliographic record
Abstract
Purpose: To describe the use of telehealth by school-based health service providers.Methods: We searched five academic databases, followed by a manual search of the reference lists of included articles. Our inclusion criteria required that articles be peer-reviewed, in English, involve use of telehealth by a health professional, and integrate services into the kindergarten to grade 12 school setting. We published an a priori protocol on Open Science Framework. Two reviewers completed article selection, followed by one reviewer and one verifier completing data extraction. We extracted a description of the articles as well as for whom, what, where, when, why, and how telehealth services were provided.Results: We screened 6585 unique sources and included 70 articles. Articles were primarily empirical (77%), from the United States (67%), and published after 2017 (73%). Telehealth services in schools were most often provided by speech-language pathologists (40%) and psychologists (40%), and were provided to students with a range of health conditions and disabilities. Telehealth services included assessment, intervention, and consultation, and were provided primarily through videoconferencing. Telehealth services were utilized to address staffing shortages, serve rural communities, and to meet COVID-19 restrictions.Conclusions: Given the heterogeneous student population and geographically limited literature, we recommend additional research to determine in what specific situations telehealth can and should be implemented.
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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 | 0.029 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.043 | 0.045 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".