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Record W2102226542 · doi:10.1016/s0304-3959(03)00263-x

Development of sensitivity to facial expression of pain

2003· article· en· W2102226542 on OpenAlexafffund
Kathleen S. Deyo, Kenneth M. Prkachin, Susan R. Mercer

Bibliographic record

VenuePain · 2003
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsUniversity of Northern British Columbia
FundersCanadian Psychological Association
KeywordsFacial expressionPsychologyDevelopmental psychologyYoung adultAudiologyPain catastrophizingChronic painClinical psychologyMedicineNeuroscienceCommunication

Abstract

fetched live from OpenAlex

The ability to perceive pain in others is an important human capacity. Its development has not been studied. The present study examined the development of sensitivity to evidence of pain from childhood to early adulthood. One hundred and thirty-four males and females from four age groups (5-6, 8-9, 11-12 years and young adult) took part. They judged the amount of pain displayed on videotaped excerpts of the facial expressions of pain patients. Excerpts were selected to display no pain, some pain and strong pain, based on facial measurements, and were displayed to participants in a signal-detection paradigm. All participant groups were more sensitive to evidence of strong than some pain. The ability to detect pain expressions increased across the young, middle and older groups of children, but older children did not differ from adults. Increasing age was generally associated with increasing sensitivity to more subtle facial signs of pain. The results indicate that the ability to perceive pain in others is already significantly developed by the ages of five to six, but refinements in the ability continue through to early adulthood. These findings represent the first description of the development of the ability to perceive pain in others. Important areas for future research into the neurobiological, personal and social determinants of this ability are highlighted.

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)0.011
metaresearch head score (Gemma)0.003
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.446
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
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 head0.019
GPT teacher head0.273
Teacher spread0.254 · 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

Citations95
Published2003
Admission routes2
Has abstractyes

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