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Record W4411428942 · doi:10.1136/ip-2024-045514

Parental perspectives of physicians and nurses on child trampoline use: “I feel like I’m just kind of caving to social pressure”

2025· article· en· W4411428942 on OpenAlexafffundabout
Michelle E. E. Bauer, Meghan Gilley, Ian Pike

Bibliographic record

VenueInjury Prevention · 2025
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of British Columbia
FundersBC Children's Hospital
KeywordsTrampolineThematic analysisSuicide preventionInjury preventionHuman factors and ergonomicsOccupational safety and healthPoison controlPsychologyMedicineNursingSocial psychologyQualitative researchEngineeringMedical emergencySociology

Abstract

fetched live from OpenAlex

BACKGROUND: A wealth of evidence demonstrates the potentially injurious consequences for children using trampolines. Despite this evidence, many parents continue to support their children's use of trampolines and instal trampolines around their homes. APPROACH: In this study, we conducted semi-structured interviews with parents across Canada who are emergency practitioners. We examined physicians' and nurses' (n=56) perspectives on their children's trampoline use. Tenets of risk society theory were used to inform our approach. RESULTS: Three themes were identified through a thematic analysis: (1) heightened injury awareness; (2) social stigma and (3) balancing child development and safety. CONCLUSION: Our findings enrich conversations on child injury prevention by demonstrating how parental attitudes towards children's use of common household features such as trampolines can be shaped by witnessing children's injuries, accessing health information and education and being exposed to public pressures to achieve parental safety ideals.

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)0.007
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.369
Teacher spread0.349 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes3
Has abstractyes

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