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Record W2078274292 · doi:10.15288/jsa.2001.62.834

Invariance of the MAST across religious groups.

2001· article· en· W2078274292 on OpenAlexaboutno aff
Susan E. Luczak, Adrian Raine, Peter H. Venables

Bibliographic record

VenueJournal of Studies on Alcohol · 2001
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersNational Institute of Mental HealthNational Institute on Aging
KeywordsMast (botany)Measurement invarianceHinduismPsychologyPopulationDemographyMedicineClinical psychologySocial psychologyStructural equation modelingMathematicsStatisticsConfirmatory factor analysisImmunologyEnvironmental healthMast cellSociologyReligious studies

Abstract

fetched live from OpenAlex

OBJECTIVE: The Michigan Alcoholism Screening Test (MAST), a commonly used instrument of alcohol-related problems, was examined to determine whether it assessed the same constructs in individuals from religions with different proscriptions regarding the use of alcohol. METHOD: The MAST was completed by participants in the longitudinal Joint Child Health Project when they were approximately 23 years old. Subjects of this study (N= 747; 505 men) were 465 Hindus, 223 Catholics and 59 Muslims who reported drinking alcohol. Measurement invariance, the determination that the same constructs are being measured across groups, was tested by comparing factor invariance using multigroup structural equation modeling. RESULTS: The Hindu and Catholic groups had similar factor structures to those found in previous Australian, Canadian and U.S. samples. Metric invariance was obtained for the Hindu and Catholic groups, but not for the Muslim group. CONCLUSIONS: These findings suggest the measurement of MAST factors is invariant across a fairly broad segment of the population in which the MAST might be used. However, the lack of invariance in this sample of Muslims suggests that the MAST is not an appropriate instrument to use among all groups of drinkers. These findings highlight the importance of testing for invariance when using psychological measures to compare heterogeneous samples.

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 score0.041
Threshold uncertainty score0.245

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 head0.072
GPT teacher head0.364
Teacher spread0.291 · 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

Citations13
Published2001
Admission routes1
Has abstractyes

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