MétaCan
Menu
Back to cohort
Record W2483428646 · doi:10.1017/s0714980816000404

Bem Sex Role Inventory Validation in the International Mobility in Aging Study

2016· article· fr· W2483428646 on OpenAlexaff
Tamer Ahmed, Afshin Vafaei, Emmanuelle Bélanger, Susan P. Phillips, Marı́a Victoria Zunzunegui

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsQueen's UniversityUniversité de Montréal
Fundersnot available
KeywordsHumanitiesGynecologyPsychologyPhilosophyMedicine

Abstract

fetched live from OpenAlex

RÉSUMÉ Cette étude, en utilisant différentes méthodes d’analyse des facteurs, a examiné la structure de mesure de l’Inventaire des rôles sexués de Bem (IRSB). La plupart des études antérieures sur la validité ont appliqué analyse factorielle exploratoire (AFE) d’examiner l’IRSB. Il s’agissait d’évaluer les propriétés psychométriques et la validité de la construction de la forme courte IRSB comprenant 12 articles dans un échantillon administré à 1,995 personnes âgées de la vague 1 de l’Initiative internationale de la mobilité en viellissement (IIMV). Nous avons utilisé l’alpha de Cronbach pour évaluer la fiabilité et la cohérence interne et une analyse factorielle confirmatoire (AFC) afin d’évaluer les propriétés psychometriques. AFE a révélé un modèle comprenant trois facteurs, qu’on a confirmé par l’AFC, puis ceci est comparé avec le modèle structurel initial de deux facteurs. Les résultats ont révélé qu’une solution à deux facteurs (instrumentalité-expression) a montré satisfaisante validité conceptuelle et un ajustement supérieur aux données, par rapport à la solution à trois facteurs. La solution à deux facteurs confirme différences attendues entre les sexes chez les personnes âgées. L’IRSB composé de 12 articles fournit un instrument bref, psychométrique et fiable dans les échantillons internationaux des personnes âgées.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.258
Teacher spread0.234 · 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

Citations32
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicGender Roles and Identity StudiesFrench-language works237,207