Effect of Virtual Reality on Pediatric Pain and Fear During Procedures Involving Needles: Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Virtual reality (VR) is used as a distraction measure during painful clinical procedures associated with the use of needles. These procedures include vaccinations, blood draws, or the administration of medications, which can cause children to feel increased levels of pain and fear. OBJECTIVE: The objective of this study was to collect and analyze the current evidence regarding the effectiveness of VR as a tool to distract children from pain and fear during needle procedures as compared to that of standard techniques. METHODS: A systematic review and meta-analysis was performed. We included randomized clinical trials (RCTs) or quasi-RCTs with participants younger than 21 years who underwent needle procedures in which the main distraction measure used was VR and where the main outcome measure was pain. The databases searched included the PubMed, Web of Science, Scopus, PsycINFO, CINAHL, and Cochrane libraries. In this systematic review, the studies were analyzed by applying the Critical Appraisal Skills Program guide in Spanish and the Jadad scale. In the meta-analysis, the effect size of the studies was analyzed based on the results for pain and fear in children. RESULTS: From 665 unique search results, 21 studies were included in this systematic review, most of which reported low methodological quality. The study sample cohorts ranged from a minimum of 15 participants to a maximum of 220 participants. Ten studies were included in the meta-analysis. The global effect of using VR as a distraction measure was a significant reduction in pain (inverse variance [IV] -2.37, 95% CI -3.20 to -1.54; Z=5.58; P<.001) and fear (IV -1.26, 95% CI -1.89 to -0.63; Z=3.92; P<.001) in children in the experimental groups. CONCLUSIONS: The quality of the studies was mostly low. The main limitations were the impossibility of blinding the participants and health care personnel to the VR intervention. Nonetheless, the use of VR as a distraction measure was effective in reducing pain and fear in children during procedures involving needles.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".