The impact of parent-targeted eHealth educational interventions on infant procedural pain management: a systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to determine if electronic health (eHealth) educational interventions for infant procedural pain and pain management impact parental outcomes (mental health outcomes, knowledge utilization outcomes, and parental involvement in care outcomes) and infant outcomes (morbidity outcomes, pain outcomes, health system outcomes). INTRODUCTION: Pain in infants is a common concern for parents. Routine postpartum care for infants in early life requires them to endure painful procedures, yet infants often receive little to no pain management. While research has shown that parents can reduce their infant's pain during procedures by breastfeeding or skin-to-skin contact, parents may not be aware of their role in pain management. Despite the recent rapid increase in eHealth resources to educate parents about infant pain management, their impact has yet to be evaluated. INCLUSION CRITERIA: This review will consider studies that include eHealth educational interventions targeted at parents during pregnancy and up to one year postpartum. All experimental study designs will be included. Primary outcomes will include: parental stress and anxiety, self-efficacy, knowledge, attitudes, eHealth intervention usage, acceptance of eHealth intervention, involvement in pain management, and infant pain response. METHODS: PubMed, CINAHL, PsycINFO, Embase, Scopus, Web of Science, and SciELO will be searched for studies published in English. Critical appraisal and data extraction will be conducted by two independent reviewers using standardized tools. Quantitative data, where possible, will be pooled in statistical meta-analysis, or if statistical pooling is not possible, the findings will be reported narratively.
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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 | 0.052 | 0.063 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.013 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".