Negative emotions and the development of alcohol and cannabis use during a social crisis: The moderating role of self-compassion.
Bibliographic record
Abstract
People experiencing a social crisis such as the COVID-19 pandemic are at risk of experiencing high levels of negative emotions stemming from this stressful event. Studies show that difficulties in regulating these emotions predispose some individuals to greater alcohol and cannabis consumption. However, self-compassion could mitigate the risks that negative emotions represent for the evolution of consumption as this psychological stance is incompatible with the underlying reasons for alcohol or cannabis use resulting from negative emotions, such as self-medication or avoidance. Self-compassion could thus act as a protective factor when confronted with negative emotions. The present study uses longitudinal data from a representative sample of the Canadian population (N=3,617). We put forward two hypotheses: 1) a high level of negative emotions will be associated with a high initial level of consumption, as well as with a consumption increase during the first year of the pandemic; 2) self-compassion will act as a protective (i.e., moderating) factor in the link between negative emotions and consumption, such that individuals experiencing a high level of negative emotions but also displaying self-compassion will have (and maintain) lower levels of consumption than individuals not reporting high self-compassion.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".