Validation of a brief behavioral economic assessment of demand among cigarette smokers.
Bibliographic record
Abstract
Basic and clinical addiction research use demand measures and analysis extensively to characterize drug use motivations. Hence, obtaining an accurate and brief measurement of demand that can be easily utilized in different settings is highly valued. In the current study, 2 versions of a breakpoint measure, designed to capture cigarette demand, were investigated in 119 smokers who were recruited from an online crowdsourcing platform. The first version determines the maximum price a smoker is willing to pay for one cigarette received right now when paid out of pocket, and the second determines the maximum price when paid using a hypothetical $100 gift card received for free. The breakpoint measures were administered along with the Cigarette Purchase Task (CPT), Fagerström Test for Cigarette Dependence (FTCD), and The Questionnaire of Smoking Urges (QSU-brief). Both single-item breakpoint versions were significantly correlated with CPT-derived demand measures loaded on the persistence factor (i.e., elasticity of demand, breakpoint, Pmax, and Omax), but not with those loaded on the amplitude factor (i.e., intensity of demand). In addition, both single-item measures were associated with metrics of tobacco dependence (e.g., FTCD, QSU) with effect sizes that are similar to the ones found between CPT-derived breakpoint and those same metrics. These findings suggest that the single-item breakpoint measure is a viable method for measuring demand that may provide a useful and efficient tool to capture crucial and distinct aspects of smoking. In addition, the breakpoint measures may help increase the utility of behavioral demand measures in novel research and clinical settings. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".