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Record W2551405228 · doi:10.12715/apr.2015.2.24

Roadside observation of child passenger restraint use

2015· article· en· W2551405228 on OpenAlexaboutno aff

Bibliographic record

VenueAdvances in Pediatric Research · 2015
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMedicine

Abstract

fetched live from OpenAlex

Background: Despite legislation and research evidence supporting the use of childhood vehicle restraints, motor vehicle crashes remain the leading cause of injury, death and disability among Canadian children. Methods: Working in collaboration with trained car seat specialists and police officers, roadside checks were conducted to observe correct use of child restraints. Results: Of the 1323 child vehicle restraints inspected, 99.6% of the children were restrained, 91% were in the correct seat, and 48% of restraints were correctly installed. The seat/restraint types most used incorrectly used were booster seats (31%) and seat belts (53%). The majority of incorrectly installed or fitted seats (55%) were forward facing. Common errors in installation and fit included the seat not being secured tightly enough to the vehicle, incorrect tether strap use, the harness not being tight enough, and/or the chest clip being in the wrong place. Conclusions: The greatest proportion of incorrect seat use was among those children who transitioned to a seat belt too soon. The greatest proportion of installation and fit errors were among forward facing seats. Researchers recommend: 1) targeting parents with older children (ages 3 and above) regarding transitioning too soon from forward facing seats to booster seats, and from booster seats to seat belts; 2) targeting parents with younger children regarding correct installation of rear facing and forward facing seats; 3) collaborating with police officers to review the most common errors and encourage observation at roadside checks; and 4) creating community awareness by way of roadside checks.

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)0.002
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 score0.097
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.162
GPT teacher head0.423
Teacher spread0.261 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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