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Record W2164346031 · doi:10.1177/1049732313505916

Heterosexual Gender Relations In and Around Childhood Risk and Safety

2013· article· en· W2164346031 on OpenAlexafffundabout
Mariana Brussoni, Lise Olsen, Genevieve Creighton, John L. Oliffe

Bibliographic record

VenueQualitative Health Research · 2013
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BCChild and Family Research Institute
KeywordsRecreationNegotiationPrivilege (computing)Developmental psychologyPsychologyPsychological interventionSuicide preventionInjury preventionPoison controlMedicineEnvironmental healthPsychiatrySociologyPolitical science

Abstract

fetched live from OpenAlex

Injuries are a leading cause of child death, and safety interventions frequently target mothers. Fathers are largely ignored despite their increasing childcare involvement. In our qualitative study with 18 Canadian heterosexual couples parenting children 2 to 7 years old, we examined dyadic decision making and negotiations related to child safety and risk engagement in recreational activities. Parents viewed recreation as an important component of men's childcare, but women remained burdened with mundane tasks. Most couples perceived men as being more comfortable with risk than women, and three negotiation patterns emerged: fathers as risk experts; mothers countering fathers' risk; and fathers acknowledging mothers' safety concerns but persisting in risk activities. Our findings suggest that contemporary involved fathering practices privilege men in the outdoors and can erode women's control for protecting children from unintentional injury. We recommend promoting involved fathering that empowers both parents and developing injury-prevention strategies incorporating both fathers' and mothers' perspectives.

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)0.013
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 score0.294
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.002
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 integrity0.0000.001
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 head0.327
GPT teacher head0.576
Teacher spread0.249 · 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

Citations37
Published2013
Admission routes3
Has abstractyes

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