Too little, too much or just right: Injury/illness sensitivity and intentions to drink as a basis for alcohol consumer segmentation
Bibliographic record
Abstract
BACKGROUND: Although alcohol is the most socially accepted drug, little is known about the classification of alcohol consumers into clusters influencing drinking outcomes. Past research has demonstrated that injury/illness sensitivity predicts health protecting behaviors. OBJECTIVES: The present study explored whether alcohol consumers can be classified based on injury/illness sensitivity and intentions to reduce drinking, and whether the identified clusters exhibited meaningful differences in negative affect and drinking levels. METHODS: Four-hundred and eighty-six participants (54.3% male; mean [SD] age = 26.5 [7.2] years) completed online questionnaires between July and October of 2017. Questions were asked pertaining to injury/illness sensitivity, intentions to reduce drinking, negative affect, and heavy drinking behavior. A k-means cluster analysis was performed on illness/injury sensitivity and intentions to reduce drinking scores. We then examined whether clusters varied according to negative affect or drinking variables. RESULTS: The k-means cluster analysis identified four clusters: Insensitive non-internalizers, Insensitive internalizers, Sensitive non-internalizers, and Sensitive internalizers. Sensitive internalizers reported the highest, whereas Insensitive non-internalizers reported the lowest, negative affect. Sensitive internalizers also had the lowest percentage of heavy drinkers. Conclusion/importance: Current findings add to the alcohol literature by indicating that high sensitivity to illnesses/injuries and the internalization of sensitivities via behavior change intentions may provide the best protection against high alcohol consumption levels.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".