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Record W2296800805 · doi:10.1002/acp.3192

A Decade Later: Adolescents' Memory for Medical Emergencies

2015· article· en· W2296800805 on OpenAlexafffund
Carole Peterson

Bibliographic record

VenueApplied Cognitive Psychology · 2015
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRecallPsychologyNarrativeMedical recordInjury preventionSuicide preventionFree recallPsychiatryDevelopmental psychologyPoison controlClinical psychologyMedicineMedical emergencySurgery

Abstract

fetched live from OpenAlex

Summary Approximately a decade earlier, 39 adolescents (3–5 years old at the time of event occurrence) were interviewed about stressful injuries serious enough to require hospital emergency room treatment. Parent and/or other witnesses were also interviewed to provide a record against which children's recall was compared. Prior to the current follow‐up, the adolescents had varying numbers of interviews (2–5), and half had been interviewed 5 years previously, whereas the remainder had not been interviewed for 8 or more years. In spite of the long delay since injury and the young age of the adolescents at the time, their recall of their injury was still excellent in terms of completeness, unique narrative detail, and accuracy, although there was a small decrease in accuracy. However, recall of hospital treatment was poorer and showed significant deterioration over time. In addition, the presence of an interview after 5 years (halfway through the 10‐year delay) as well as the number of interviews had no significant effect on 10‐year recall of either event, although more interviews tended to make free recall of the injury more detailed.Copyright © 2015 John Wiley & Sons, Ltd.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.084
GPT teacher head0.377
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2015
Admission routes2
Has abstractyes

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