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Record W2887341399 · doi:10.29359/bjhpa.10.2.13

Polish adaptation and validation of the Anti-Fat Attitudes Scale – AFAS

2018· article· en· W2887341399 on OpenAlexaboutno aff
Małgorzata Obara-Gołębiowska, Justyna Michałek-Kwiecień

Bibliographic record

VenueBaltic Journal of Health and Physical Activity · 2018
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsnot available
Fundersnot available
KeywordsConfirmatory factor analysisPsychologyStructural equation modelingScale (ratio)Exploratory factor analysisConstruct validityOverweightReliability (semiconductor)ValidityPsychometricsStatisticsDevelopmental psychologyMathematicsMedicineBody mass indexGeographyCartography

Abstract

fetched live from OpenAlex

Background: Bias, stigma, and discrimination in relation to weight are frequently experienced by many obese people. The goal of the present study was to develop a Polish adaptation of the Anti-Fat Attitudes Scale (AFAS) proposed by Morrison and O'Connor. Materials/methods: The study was conducted on undergraduate students of the University of Warmia and Mazury in Olsztyn. The original Canadian Scale Anti-Fat Attitudes Scale was translated into Polish, and its factor structure, reliability and construct validity were determined. Results: The exploratory factor analysis (Study 1) supported the development of the Polish version of the Anti-Fat Attitudes Scale with a one-dimensional structure modeled on the original version of the scale (factor loading ranged from .71 to .85). The confirmatory factor analysis (Study 2) validated the one-factor structure of the tool with high values of GFI and AGFI (above .95) and an acceptable value of RMSEA (RMSEA =.07). The results of the analysis revealed that satisfactory stability was maintained over a 4-week period. The validity criterion was confirmed based on correlations with the constructs that were theoretically linked to this phenomenon. Conclusions: The Polish version of the AFAS can be used to measure negative attitudes toward overweight individuals.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.238
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.098
GPT teacher head0.452
Teacher spread0.354 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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