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100 Establishing the effectiveness of a storybook for teaching home safety to preschool children

2020· article· en· W3027723764 on OpenAlexaff
Barbara A. Morrongiello

Bibliographic record

VenuePoster presentations · 2020
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIntervention (counseling)HazardReading (process)Psychological interventionPsychologyHazard analysisDevelopmental psychologyEngineeringPsychiatry

Abstract

fetched live from OpenAlex

<h3>Statement of Purpose</h3> Unintentional injuries are a serious concern for young children, frequently resulting in disability and death. Safety interventions that target the child as an agent of change are needed, particularly because parents cannot constantly supervise. The present study examined whether a storybook can educate about home hazards and reduce the hazard-directed risk behaviours of children aged 3.5 to 5.5 years. <h3>Methods</h3> Preschoolers were randomly assigned to the control condition (a storybook about healthy eating) or the intervention condition (a storybook about home hazards) and were required to read the assigned storybook with their mother for four weeks. <h3>Results</h3> Comparing children’s pre- and post-intervention knowledge and risk behaviours indicated that children in the intervention condition were able to identify more hazards, provide more comprehensive explanations, and demonstrate less risky behaviours, in comparison to those in the control group. Hence, the storybook improved both safety knowledge and behaviors. <h3>Conclusion</h3> Together, the findings suggest that a storybook can be an effective resource in educating young children about home safety and promoting safety practices that are likely to reduce risk of injury. <h3>Significance</h3> The findings indicate that engaging children in reading a storybook about home hazards with their parents can not only increase their knowledge but also reduce their hazard-directed risk behaviors at home.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.345
Teacher spread0.320 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2020
Admission routes1
Has abstractyes

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