Digital health solutions for caregivers of children experiencing acute pain: a scoping review
Bibliographic record
Abstract
Digital health solutions are often used to support caregivers in managing acute pain in their children. The aim of this scoping review was to identify, characterize, and synthesize the literature on caregiver-targeted digital health solutions for acute pain management in children as it relates to caregiver, child, and implementation outcomes. Studies must have described a caregiver-targeted digital health solution for acute pain management in children that provides information or support to caregivers of children experiencing acute pain (eg, needle-related procedure, injury, medical procedure). Studies that included caregivers of children 0 to 19 years were eligible. Following the Joanna Briggs Institute scoping review methodology, MEDLINE, Embase, CINAHL, and PsycINFO databases were searched from January 1, 2010 to May 20, 2025. Two reviewers completed screening and data extraction, and a third reviewer resolved disagreements. From 8949 records screened, 33 studies were eligible. From these, there were 15 individual digital health solutions identified. Of the 7,125 caregivers, 95.2% were mothers. More than half of digital health solutions targeted caregivers of newborns (0-1 years, 60.6%), and all except 2 focused on acute pain during needle-related procedures (eg, vaccinations). Overall, 21 studies reported on caregiver outcomes (primarily knowledge), 11 reported on child outcomes (primarily pain and distress), and 28 reported on implementation outcomes (primarily adoption and acceptability). There is a need for (1) standardization in the evaluation of digital health solutions for caregivers, (2) digital health solutions that target acute pain other than needle-related procedures, and (3) increased diversity of caregivers (eg, non-White, non-mothers) and solutions (eg, culturally diverse).
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".