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Record W4210836293 · doi:10.21203/rs.3.rs-1148688/v1

Functionality Appreciation Scale (FAS): Item Response Theory Examination

2022· preprint· en· W4210836293 on OpenAlexaboutno aff
Joshua Marmara, Daniel Zarate

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsItem response theoryDifferential item functioningScale (ratio)Logistic regressionPerceptionPsychologyReliability (semiconductor)Sample (material)Social psychologyClinical psychologyDemographyPsychometricsStatisticsPower (physics)GeographySociologyMathematicsCartography

Abstract

fetched live from OpenAlex

Abstract Background: The present study considers a measure of positive body image, the Functionality Appreciation Scale (FAS), which assesses one’s perception of appreciating, respecting, and honouring the body for what it can do [3]. Differential functioning of the scale across groups (i.e., gender) is yet to be investigated. The present study contributes to this area of knowledge via the employment of Item Response Theory (IRT) analyses. Methods: A sample of 386 adults from Canada, Australia, New Zealand, Ireland, the United Kingdom (UK), and United States of America (USA) were assessed online (N = 394, 54.8% men, 43.1% women, M age = 27.48, SD = 5.57). Results: The two-parameter logistic model employed to observe IRT properties indicated that all items demonstrated, although variable, strong discrimination capacity. Considering the DIF across men and women, all items demonstrated psychometric invariance across groups indicating that FAS measures FA equally in men and women. Conclusions: The items showed increased reliability for latent levels of ± 2 SD from the mean level of Functionality Appreciation. The implications and interpretations of the findings for clinical practice are discussed.

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)0.023
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.013
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 science0.0000.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.092
GPT teacher head0.443
Teacher spread0.351 · 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.

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

Citations7
Published2022
Admission routes1
Has abstractyes

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