Training Service Users in the Use of Telehealth: Scoping Review
Bibliographic record
Abstract
BACKGROUND: The use of telehealth has rapidly increased, yet some populations may be disproportionally excluded from accessing and using this modality of care. Training service users in telehealth may increase accessibility for certain groups. The extent and nature of these training activities have not been explored. OBJECTIVE: The objective of this scoping review is to identify and describe activities for training service users in the use of telehealth. METHODS: Five databases (MEDLINE [via PubMed], Embase, CINAHL, PsycINFO, and Web of Science) were searched in June 2023. Studies that described activities to train service users in the use of synchronous telehealth consultations were eligible for inclusion. Studies that focused on health care professional education were excluded. Papers were limited to those published in the English language. The review followed the Joanna Briggs Institute guidelines for scoping reviews and was reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Titles and abstracts were screened by 1 reviewer (EG). Full texts were screened by 2 reviewers (EG and JH or SC). Data extraction was guided by the research question. RESULTS: The search identified 8087 unique publications. In total, 13 studies met the inclusion criteria. Telehealth training was commonly described as once-off preparatory phone calls to service users before a telehealth visit, facilitated primarily by student volunteers, and accompanied by written instructions. The training content included guidance on how to download and install software, troubleshoot technical issues, and adjust device settings. Older adults were the most common target population for the training. All but 1 of the studies were conducted during the COVID-19 pandemic. Overall, training was feasible and well-received by service users, and studies mostly reported increased rates of video visits following training. There was limited and mixed evidence that training improved participants' competency with telehealth. CONCLUSIONS: The review mapped the literature on training activities for service users in telehealth. The common features of telehealth training for service users included once-off preparatory phone calls on the technical elements of telehealth, targeted at older adults. Key issues for consideration include the need for co-designed training and improving the broader digital skills of service users. There is a need for further studies to evaluate the outcomes of telehealth training activities in geographically diverse areas.
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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 | 0.041 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.027 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".