Parent-Targeted Education Regarding Infant Pain Management Delivered During the Perinatal Period
Bibliographic record
Abstract
All infants experience pain in early life from procedures. Parents recognize pain as a prevalent issue, reporting a strong desire for more information on infant pain. The aim of this study was to explore and map the current evidence of parent-targeted educational interventions about infant pain, delivered throughout the perinatal period. Records were identified in PubMed, CINAHL, EMBASE, and ERIC databases and hand searching recent publications in 3 relevant journals. Records in English that described or evaluated educational interventions on infant pain management aimed at parents during the perinatal period were eligible for review and those not related to pain or aimed at healthcare providers were excluded. Evaluation was completed following the Methodology for JBI Scoping Reviews and standardized critical appraisal instruments from the Joanna Briggs Institute. Initial search yielded 6946 records, with 9 included in analysis. Six studies were quantitative, 2 qualitative, and 1 mixed methods. Included interventions contained information about parent-led pain management strategies for infants in the neonatal intensive care unit (n = 4), full term (n = 4), or both (n = 1). Despite being an area of high concern for parents of newborns, few studies addressed parent-targeted education regarding infant pain. Future research examining the impact and efficacy of these interventions addressing parental and neonatal outcomes is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".