Electronic Communication Between Children’s Caregivers and Health Care Teams: Scoping Review on Parental Caregiver’s Perceptions and Experience
Bibliographic record
Abstract
Background: Asynchronous communication via electronic modes (e-communication), including patient portals, secure messaging services, SMS text messaging, and email, is increasingly used to supplement synchronous face-to-face medical visits; however, little is known about its quality in pediatric settings. Objective: This review aimed to summarize contemporary literature on pediatric caregivers' experiences with and perspectives of e-communication with their child's health care team to identify how e-communication has been optimized to improve patient care. Methods: A scoping review following the Arksey and O'Malley methodological framework searched PubMed, CINAHL, Embase, and Web of Science using terms such as "Electronic Health Records" and "Communication" from 2013 to 2023 that discussed caregiver experiences and perspectives of e-communication with their child's health care provider. Studies were excluded if they were abstracts, non-English papers, nonscientific papers, systematic reviews, or quality improvement initiatives, or pertained to synchronous telemedicine. We conducted a two-step screening process by scanning the title and abstract and reviewing the full text by two independent screeners to confirm eligibility. From an initial 903 articles identified via the database search, 23 articles fulfilled all the inclusion criteria and are included in this review. Results: Of the 23 articles meeting the inclusion criteria, 11 used quantitative methods, 7 used qualitative methods, and 5 used mixed methods. The caregiver sample sizes ranged from 51 to 3339 in the quantitative studies and 8 to 36 in the qualitative and mixed methods studies. A majority (n=17) used the patient portal that was self-categorized by the study. Secure messaging through a portal or other mobile health app was used in 26% (n=6) of the studies, while nonsecure messaging outside of the portal was used 17% (n=4) of the time and email was used 33.3% (n=8) of the time. In 19 of the studies, parents reported positive experiences with and a desire for e-communication methods. Conclusions: The literature overwhelmingly supported caregiver satisfaction with and desire for e-communication in health care, but no literature intentionally studied how to improve the quality of e-communication, which is a critical gap to address.
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".