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Record W2138781779 · doi:10.1136/ip.2010.029215.326

Developing guidelines for interventions to reduce risk of low-speed vehicle run-overs of young children

2010· article· en· W2138781779 on OpenAlexaboutno aff
Kerry Armstrong, Patricia L. Obst, Jeremy D. Davey, Hanna Thunström

Bibliographic record

VenueInjury Prevention · 2010
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionIntervention (counseling)JudgementQuarter (Canadian coin)Promotion (chess)Poison controlOccupational safety and healthSuicide preventionInjury preventionHuman factors and ergonomicsPsychologyQualitative researchPerceptionApplied psychologyEngineeringForensic engineeringMedicineEnvironmental healthPsychiatryPolitical scienceSociologyGeography

Abstract

fetched live from OpenAlex

In Australia, research suggests that up one quarter of child pedestrian hospitalisations result from driveway run-over incidents, with the parent or family member of the child being most likely the driver of the vehicle at the time of the incident. As such, driveway run-over incidents are an important issue that need to be addressed through public health educative initiatives. A series of qualitative interviews were conducted in order to assess general behavioural and environmental changes that parents/carers had specifically undertaken in order to reduce the risk of injury to any child in their care. A second phase of the interviews was also conducted and focused on parent/carers perceptions and attitudes of the risk of a child in their care being involved in a driveway run-over incident. The interviews elicited three main themes which together built a robust model representing the main aspects parents take into consideration in making a judgement concerning the safety of their child. The first aspect concerned the safety of the domestic environment. The second aspect concerned the level of supervision a child received, whereas the third included the child's ability to understand and comply with rules, as well as maturity of risk perception and skills. The model developed from this research has direct applicability for the development and promotion of an effective intervention in order to reduce the risk of a driveway run-over incident occurring.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.029
GPT teacher head0.328
Teacher spread0.300 · 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 designBench or experimental
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

Citations1
Published2010
Admission routes1
Has abstractyes

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