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Record W2052909604 · doi:10.1136/ip.2010.029215.271

Fathers' child injury prevention attitudes and practices

2010· article· en· W2052909604 on OpenAlexaffabout
Mariana Brussoni, Lise Olsen, D Sheftel, M. Anne George

Bibliographic record

VenueInjury Prevention · 2010
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInjury preventionOccupational safety and healthSuicide preventionHuman factors and ergonomicsPoison controlPerceptionQualitative researchPsychologyRelevance (law)Grounded theoryMedicineDevelopmental psychologyEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

Introduction Fathers have a major impact on various aspects of child health and development yet little is known about their roles in preventing injuries the most significant risk to their childrens health. While parents play a large role in mitigating childhood injury risks, particularly for younger children, the role of each parent differs substantially and requires investigation. Purpose Using a qualitative approach to develop to enhance our understanding of fathers safety-related attitudes and practices. Method Interviews were conducted with fathers of children aged 2–7 years in British Columbia, Canada. Questions addressed fathers roles and typical activities with their children, concerns regarding child safety, safety practices, and access of safety-related resources. Grounded theory methods guided data analysis. Results A diverse sample of 32 fathers was interviewed. Central themes expressed by fathers included: exposure to risk plays an important role in childrens lives; accidents are part of life; not all injuries can be prevented; preventing serious injuries is important; injuries can represent learning experiences. Fathers focused on outdoor risk, rather than home-based risks and used supervision as an important strategy for injury prevention. Conclusion Fathers attitudes and practices are important to consider in designing prevention programs. Building on perceptions that promote injury prevention (eg, use of supervision), and considering perceptions that challenge the perceived need for prevention in programming (eg, acceptance of injuries and risk) can help ensure the relevance and success of messages.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.492
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.394
Teacher spread0.369 · 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
Published2010
Admission routes2
Has abstractyes

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