“It Was the Best of Times, It Was the Worst of Times”: A Qualitative Investigation of Perfectionism and Drinking Narratives in Undergraduate Students
Bibliographic record
Abstract
Perfectionism is a transdiagnostic risk factor for mental health and interpersonal difficulties, but research on perfectionism and alcohol use in emerging adults remains equivocal. Qualitative research methods are underutilized in this area, and inductive analysis of drinking narratives in undergraduate perfectionists may help clarify conflicting results and support novel approaches to quantitative inquiry in this area. We interviewed 20 undergraduates high in perfectionism (6 adaptive perfectionists and 14 maladaptive perfectionists) using a narrative interview, with analyses focusing on a situation involving alcohol use. We coded interviews for emergent themes using thematic analysis. Five themes emerged as follows: (1) drinking as a social experience, (2) suffering consequences, (3) learning from alcohol, (4) alcohol use as escapism, and (5) reluctance and moderation. Our results add to existing literature by highlighting the interpersonal conflict in perfectionistic people's experience in relation to alcohol use during emerging adulthood. Results also suggest perfectionistic people may use alcohol and intoxication as a way to facilitate a "release" from unpleasant situations or emotions. Perfectionists reported both positive and negative experiences, which lends support for using a narrative perspective to help overcome preexisting assumptions about adaptive and maladaptive qualities of perfectionism.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".