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Record W3080453774 · doi:10.1177/1367493520951302

Ability of 3- to 5-year-old children to use simplified self-report measures of pain intensity

2020· article· en· W3080453774 on OpenAlexaff
Tiina Jaaniste, Ashleigh Burgess, Mathushinee Mohanachandran, Carl L. von Baeyer, G. David Champion

Bibliographic record

VenueJournal of Child Health Care · 2020
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsOrdinal ScaleContext (archaeology)PsychologyScale (ratio)LoudnessOrdinal dataPain scaleSeriation (archaeology)Developmental psychologyStatisticsAudiologyMathematicsMedicinePhysical therapy

Abstract

fetched live from OpenAlex

Little is known about self-report pain intensity scales best suited for young children. We tested the ability of preschool children to use two simplified scales (concrete ordinal and faces). Three- to 5-year-olds ( n = 123) were asked to make binary discriminations (‘less’ vs ‘more’ pain) between response options using the Simplified Faces Pain Scale and Simplified Concrete Ordinal Scale and to complete a seriation task. Eighty participants were also asked to use the Simplified Concrete Ordinal Scale, with modified verbal anchors, to rate the loudness of tones and to assess practice effects. Binary discrimination accuracy and seriation ability improved with age. When using the Simplified Concrete Ordinal Scale to rate the loudness of tones, even the 3-year-olds performed significantly better than chance, and performance was better in 4- and 5-year-olds. Little evidence supported the ability of 3-year-olds to use either of the simplified tools in the pain context. The 4-year-olds demonstrated greater accuracy in using the Simplified Concrete Ordinal Scale than the Simplified Faces Pain Scale, suggesting that the Simplified Concrete Ordinal Scale may be more appropriate for this age group.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.082
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.311
Teacher spread0.283 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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