When asking ‘are you stressed?’ is not enough: Hair cortisol, subjective stress, and alcohol use during the first year of the pandemic
Bibliographic record
Abstract
The onset of the COVID-19 pandemic was accompanied by an increase in alcohol use in a third of the population worldwide. To date, the literature shows that subjective reports of stress predicted increased alcohol use during the early stages of the pandemic. However, no studies have investigated the effect of physiological stress (via the stress hormone cortisol) on alcohol use during the pandemic. This study aimed to identify the predictive value of cortisol and/or subjective stress on alcohol use during the first year of the pandemic. Every three months, between June 2020 and March 2021, 79 healthy adults (19-54 years old) answered online questionnaires assessing alcohol use. In May 2020, participants reported pre-pandemic alcohol use, while in June 2020, participants reported current alcohol use, subjective stress measures, and provided a 6 cm hair sample. The latter allowed us to quantify the cumulative levels of cortisol produced in the three months prior to and following the start of the mandatory lockdown measures in March 2020 in Quebec, Canada. A relative change in hair cortisol was computed to quantify the physiological stress response. While controlling for sex, age, and psychiatric diagnoses, multilevel linear regressions revealed that alcohol use increased only among people with concomitant high subjective stress and elevated hair cortisol concentrations. Moreover, this increased alcohol use remained elevated one year later. This study documents the importance of simultaneously considering stress biomarkers and subjective stress to identify people at risk of increasing their alcohol use during major stressful life events.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".