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Record W2760173501 · doi:10.1177/0009922817733694

Procedural Pain: Systematic Review of Parent Experiences and Information Needs

2017· review· en· W2760173501 on OpenAlexafffund
Allison Gates, Kassi Shave, Robin Featherstone, Kelli Buckreus, Samina Ali, Shannon D. Scott, Lisa Hartling

Bibliographic record

VenueClinical Pediatrics · 2017
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMedicineVenipuncturePhlebotomyMedical recordQualitative researchMEDLINENursingFamily medicineSurgery

Abstract

fetched live from OpenAlex

Parents wish to reduce their child's pain during medical procedures but may not know how to do so. We systematically reviewed the literature on parents' experiences and information needs related to managing their child's pain for common medical procedures. Of 2678 records retrieved through database searching, 5 were included. Three additional records were identified by scanning reference lists. Five studies were qualitative, and 3 were quantitative. Most took place in North America or Europe (n = 7) and described neonatal intensive care unit experiences (n = 5). Procedures included needle-related medical procedures (eg, venipuncture, phlebotomy, intravenous insertion), sutures, and wound repair and treatment, among others. Generally, parents desired being present during procedures, wanted to remain stoic for their child, and thought that information would be empowering and relieve stress but felt unsupported in taking an active role. Supporting and educating parents may empower them to lessen pain for their children while undergoing medical procedures.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0110.013
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.149
GPT teacher head0.454
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations55
Published2017
Admission routes2
Has abstractyes

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