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Record W2219382168 · doi:10.1093/jpepsy/jsv111

Children’s Forgetting of Pain-Related Memories

2015· article· en· W2219382168 on OpenAlexafffund
Tammy A. Marche, Jennifer L. Briere, Carl L. von Baeyer

Bibliographic record

VenueJournal of Pediatric Psychology · 2015
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health Research
KeywordsForgettingPsychologyTask (project management)Coping (psychology)Developmental psychologyRetrieval-induced forgettingMotivated forgettingCognitive psychologyClinical psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: Given that forgetting negative experiences can help children cope with these experiences, we examined their ability to forget negative aspects of painful events. METHODS: 86 children aged 7-15 years participated in a retrieval-induced forgetting task whereby they repeatedly retrieved positive details of a physically painful experience, and an experimental pain task (cold-pressor task). RESULTS: Repeatedly retrieving positive details of a prior pain experience produced forgetting of the negative aspects of that experience. Pain-related self-efficacy predicted retrieval-induced forgetting; children with a poorer belief in their ability to cope with pain experienced less forgetting. Children who had a more difficult time forgetting prior negative experiences were more anxious about the pain task and reported higher pain thresholds. CONCLUSIONS: Understanding children's memory for painful experiences may help improve their pain management and coping ability.

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)0.006
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.056
GPT teacher head0.335
Teacher spread0.279 · 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

Citations31
Published2015
Admission routes2
Has abstractyes

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