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Record W2805112000 · doi:10.1016/j.abrep.2018.05.001

Psychometric evaluation of a lifetime version of the marijuana problems scale

2018· article· en· W2805112000 on OpenAlexaff
David C. Hodgins, Jonathan N. Stea

Bibliographic record

VenueAddictive Behaviors Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychologyReliability (semiconductor)CannabisClinical psychologyScale (ratio)Psychiatry

Abstract

fetched live from OpenAlex

The Marijuana Problems Scale (MPS) is a widely-used self-report measure of cannabis-related negative consequences that has a past three-month reporting window. This report describes the psychometric characteristics of a lifetime version (MPS-L). As part of a larger study, 119 individuals who had recovered from cannabis use disorder completed the MPS-L on two occasions 2 weeks apart and 91 participant-nominated family and friends also completed a collateral version of the scale. Item analyses and principal component analysis (PCA) revealed that three of the 19 items were relatively weaker. Omitting these items, the MPS-L showed good internal reliability (α = 0.88, for summed severity total, α = 0.85 for number of consequences identified) and test-retest reliability (r = 0.81 and 0.73). As expected, correlations with collateral reports were moderate (r = 0.33 and 0.29), and collaterals reported significantly fewer negative consequences than participants. MPS total scores also correlated as expected with external validity measures (e.g., number of cannabis use disorder symptoms reported, motives for use, lifetime depression, treatment history). PCA supported the use of a total score summed score, but also revealed two secondary factors, measuring internal consequences (e.g., low self-esteem) and external consequences (e.g., financial difficulties). These analyses provide good preliminary support for a lifetime version of the MPS, with the summed severity total score performing slightly better than the total number of consequences endorsed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.334
Teacher spread0.309 · 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.

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

Citations25
Published2018
Admission routes1
Has abstractyes

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