MétaCan
Menu
Back to cohort
Record W4401571575 · doi:10.1177/07342829241273257

Psychometric Properties and Factorial Structure of General Mattering Scale, Anti-Mattering Scale, and Fear of Not Mattering Inventory Within the Palestinian Context

2024· article· en· W4401571575 on OpenAlexaff
Fayez Mahamid, Gordon L. Flett, Masood Zangeneh, Dana Bdier

Bibliographic record

VenueJournal of Psychoeducational Assessment · 2024
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsHumber PolytechnicYork University
Fundersnot available
KeywordsPsychologyScale (ratio)Context (archaeology)FactorialPsychometricsSocial psychologyFactorial analysisClinical psychologyStatisticsMathematicsPhysics

Abstract

fetched live from OpenAlex

The current study examined the psychometric properties and correlates of three measures assessing individual differences in mattering among people from Palestine assessed in January, 2024. This study uniquely considers mattering as a resource and feelings of not mattering as a risk factor among people experiencing traumatizing life circumstances. Our sample consisted of 950 Palestinian adults (305 men and 645 women). They completed the General Mattering Scale, the Anti-Mattering Scale, the Fear of Not Mattering Inventory, and the Depression Anxiety Stress Scales-21 (DASS-21). Extensive psychometric tests supported these measures as each having one factor with adequate reliability and validity. Examination of means indicated significantly elevated levels of fear of not mattering, anxiety, depression, and stress. Regression analyses further established that each measure predicted significant unique variance in anxiety, depression, and stress. The findings attest to the further use of these measures and how feelings of mattering can be vital resource as the need for resilience and adaptability escalates due to traumatizing events.

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 score0.311
Threshold uncertainty score0.509

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 head0.060
GPT teacher head0.364
Teacher spread0.303 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Psychoeducational AssessmentSame topicEmotional Intelligence and PerformanceFrench-language works237,207