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Record W4406378045 · doi:10.1016/j.heliyon.2025.e41956

Development and validation of the gift reciprocation anxiety scale (GRAS) for youths and adults in intimate relationships

2025· article· en· W4406378045 on OpenAlexfundno aff
Mohd. Ashik Shahrier

Bibliographic record

VenueHeliyon · 2025
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsnot available
FundersCanada Foundation for Innovation
KeywordsAnxietyPsychologyScale (ratio)Gift givingClinical psychologyAdvertisingSocial psychologyPsychiatryPolitical scienceBusiness

Abstract

fetched live from OpenAlex

= 763). Next, the adequacy of the data for factor analysis was checked and exploratory factor analysis (EFA) was done, extracting a single factor structure which was confirmed through the same factor retention using parallel analysis (PA). Model fit indices of confirmatory factor analysis (CFA) validated the unifactorial solution of GRAS. In addition, the item response theory (IRT) analyses confirmed that the items of the GRAS had high discriminative power, satisfactory threshold parameters, and covered a wide range of the latent trait. Mean inter-item correlations, corrected item-total correlations, and internal consistency reliabilities of the newly developed GRAS fall within the suggested limits. Multi-group confirmatory factor analysis (MGCFA) revealed that the GRAS can invariably be applied across gender, age, and marital status. A moderately positive association of GRAS with reciprocity anxiety, depression, and anxiety indicated the convergent validity of the scale. Altogether, GRAS has been found to be a psychometrically sound tool to objectively measure gift reciprocation anxiety in close relationships, implicating gift reciprocation less as an obligation and more as signs of trust, commitment, security, and care for ensuring better intimate relationships.

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)0.000
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 score0.023
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.340
Teacher spread0.316 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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