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Record W4322207913 · doi:10.1080/23311908.2023.2180872

Self-efficacy and alcohol consumption: Are efficacy measures confounded with motivation?

2023· article· en· W4322207913 on OpenAlexaff
Tom St Quinton, Ben Morris, Alexander Lithopoulos, Paul Norman, Mark Conner, Ryan E. Rhodes

Bibliographic record

VenueCogent Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsVignetteSelf-efficacyPsychologyClinical psychologyAlcohol consumptionSocial psychologyAlcohol

Abstract

fetched live from OpenAlex

Recent research has suggested self-efficacy measures (i.e., I can) are confounded with motivation (i.e., I will). The study tested whether two measurement conditions can disentangle motivation from self-efficacy in relation to alcohol consumption. Specifically, the study compared a standard self-efficacy measurement condition with a motivation held constant (i.e., including “If I really wanted to” in self-efficacy measures) and a vignette condition (i.e., clarifying the definition of “can” before self-efficacy measurements). A randomized posttest-only design was used. A sample of 259 university students were allocated to one of three conditions (standard; motivation held constant; vignette) and completed measures of self-efficacy and alcohol consumption. Greater self-efficacy towards both consuming and refraining from alcohol was found in the vignette (d = 0.58 & 0.74) and motivation held constant (d = 0.34 & 0.58) conditions. Heavy drinkers in the vignette (d = 1.48) and motivation held constant (d = 0.93) conditions reported greater self-efficacy for refraining from alcohol than the standard condition. Self-efficacy towards refraining from alcohol in the standard condition (r = −.55) was more highly correlated with alcohol behaviour than self-efficacy in the vignette condition (r = −.06). The study adds to the evidence that standard measures of self-efficacy are confounded with motivation. Providing a vignette clarifying the meaning of self-efficacy and including “If I really wanted to” in self-efficacy measures can overcome self-efficacy confounding.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.999

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)0.0020.003

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 head0.200
GPT teacher head0.445
Teacher spread0.245 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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