Impacts of the COVID-19 public health restrictions on substance use, mental health, and psychosocial functioning among individuals with alcohol use disorder
Bibliographic record
Abstract
Background: The COVID-19 pandemic has been associated with major psychosocial disruptions and there is particular concern for individuals with substance use disorders. Objectives: This study characterized the psychosocial and experiential impacts of the pandemic on individuals seeking alcohol use disorder (AUD) recovery, including pandemic impacts on self-reported drinking, heavy drinking, tobacco, cannabis, and stimulant use. Methods: Participants were 125 AUD+ individuals (% males: 57.60; Mage = 49.11, SD = 12.13) reporting on substance use from January 1st–24th March, 2020 (pre-pandemic) and since the stay-at-home orders commenced, 24th March–June 28th 2020 (intra-pandemic). Within-subjects changes were examined and a latent profile analysis was performed to identify subgroups differentially impacted by the pandemic. Results: Large proportions reported psychosocial impacts of COVID-19, but drinking and other substance use did not reveal significant changes. Latent profile analyses revealed two subgroups: Profile 1 (n = 41/125), “Moderately Impacted”) and Profile 2 (n = 84/125), “Severely Impacted”). Compared to the pre-pandemic period, the group that was moderately impacted by the pandemic exhibited significantly fewer heavy drinking days (p = .02) during the intra-pandemic period, but no other substance use changes. The group showing severe pandemic impacts did not exhibit changes in alcohol or other drug use but evidenced more severe anxiety and depression (ps < .001). Conclusions: We found heterogeneous subtypes of pandemic-related impacts in AUD recovery patients. There is need to provide psychosocial support to this particular population and further monitoring substance use and mental health.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".