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Record W3195394729 · doi:10.1093/jpepsy/jsab032

Understanding Infants’ In-Home Injuries: Context and Correlates

2021· article· en· W3195394729 on OpenAlexafffund
Barbara A. Morrongiello, Michael Corbett, Lindsay Bryant, Amanda Cox

Bibliographic record

VenueJournal of Pediatric Psychology · 2021
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of Guelph
FundersSocial Sciences and Humanities Research Council
KeywordsInjury preventionContext (archaeology)Occupational safety and healthHuman factors and ergonomicsPoison controlMedicineSuicide preventionCrawlingSittingMedical emergencyPsychologyPhysical therapy

Abstract

fetched live from OpenAlex

RATIONALE: Infancy is a time of elevated risk of injury. Past research has focused mostly on the type of injuries, leaving many gaps in knowledge about contextual information that could aid in injury prevention planning. METHODS: In this longitudinal study, a participant-event recording method was used in which mothers tracked their infants' home injuries through three motor development stages (sitting up independently, crawling, and walking). A contextual analysis elucidated where injuries occurred, their type and severity, the infant's and parent's behaviors at the time, if the infant had done the risk behavior before and been injured, the level of supervision, and the nature of any safety precautions parents implemented following these injuries. RESULTS: Injuries occurred as often in play as in nonplay areas and were due to physically-active nonplay activities more so than play activities; mothers were often doing chores. Bumps and bruises were the most common types of injuries. As infants became more mobile, supervision scores declined and injury severity scores increased. Infants had done the risk behavior leading to injury previously about 60% of the time, with higher scores associated with parents implementing fewer preventive actions in response to injury. When mothers did implement a safety precaution, greater injury severity was associated with more modifications to the environment and increased supervision; teaching about safety was infrequent. CONCLUSION: Implications of these results for injury prevention messaging are discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.075
GPT teacher head0.373
Teacher spread0.298 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

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