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Record W4381741405 · doi:10.3390/children10071097

Picturing Bravery: A Rapid Review of Needle Procedures Depicted in Children’s Picture Books

2023· review· en· W4381741405 on OpenAlexafffund
Hiba Nauman, Olivia Dobson, Anna Taddio, Kathryn A. Birnie, C. Meghan McMurtry

Bibliographic record

VenueChildren · 2023
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsWestern UniversityUniversity of TorontoMcMaster Children's HospitalUniversity of CalgaryUniversity of Guelph
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsArtVisual artsPicture books

Abstract

fetched live from OpenAlex

Existing research has identified evidence-based strategies for mitigating fear and pain during needle procedures; yet, families often experience limited access to health professionals who deliver these interventions. Children may benefit from learning about such strategies in a developmentally appropriate and accessible format such as a picture book. This review aimed to summarize content related to needle procedures represented in picture books for 5- to 8-year-old children. Key terms were searched on Amazon, and the website was used to screen for relevant eligibility criteria. Three levels of screening and exclusions resulted in a final sample of 48 books. Quantitative content analysis was used to apply a coding scheme developed based on relevant Clinical Practice Guidelines and systematic reviews. Cohen's Kappa indicated strong reliability, and frequencies were calculated to summarize the content. The books were published between 1981 and 2022. All 48 books included at least one evidence-based coping strategy. Distressing aspects such as scary visuals were often included (27.1%), as well as specific expressions of fear (52.1%) and pain (16.7%). Overall, this study paves the way for researchers interested in evaluating the effectiveness of picture books on children's knowledge and self-efficacy, as well as creating interventions for coping.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.368
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.316
Teacher spread0.287 · 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 teacher head, not a consensus.

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

Citations9
Published2023
Admission routes2
Has abstractyes

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