#3123 Assessing international alcohol and internet use patterns during COVID-19 isolation using an online survey: highlighting distinct stressors conferring risk to compulsive behaviours
Bibliographic record
Abstract
Objectives and Aims The Coronavirus (COVID-19) pandemic has required drastic safety measures to contain virus spread, including an extended self-isolation period. Those with greater perceived or actual life stress are vulnerable to develop or reinstate problematic behaviours characterised by addiction and compulsive mechanisms. Thus, we assessed how the COVID-19 pandemic and isolation measures affected alcohol consumption and internet use in the general population. Methods We developed an online international survey, entitled Habit Tracker (HabiT), completed by 1,346 adults (≥18 years), which measured changes in amount and severity of alcohol consumption (Alcohol Use Disorders Identification Test; AUDIT),online gaming (Internet Gaming Disorder Scale-Short Form; IGDS9-SF), and pornography viewing (Cyber Pornography Addiction Test; CYPAT) before (post-hoc recall)and during the COVID-19 pandemic and consequent lockdown. These measures were related to ten COVID-19-specific stress factors. Lastly, we assessed psychiatric factors widely recognized to be associated with problematic alcohol and internet use such as anxiety, depression (Hospital Anxiety and Depression Scale; HADS), and impulsivity (Short Impulsive-Behavior Scale; SUPPS-P). Results Of the sample, we observed an overall increase in online gaming and a decrease alcohol consumption and pornography viewing. Those who increased their amount and severity of alcohol use (36%) during lockdown reported stress associated with the pandemic itself, such as being an essential worker directly caring for those with or having a loved one become severely ill from COVID-19. Further, those residing in the United Kingdom- as opposed the United States or Canada- increased their weekly amount of alcohol consumption. Alternatively, those who increased online gaming (64%) and pornography viewing (43%)reported low frequency or poor quality social interactions resultant of lockdown measures. All three groups displayed higher levels of depression, anxiety, and urgency impulsivity. Conclusions Our findings underscore the theoretical mechanism of negative emotionality underlying forms of compulsive behaviour driven by stress, depression, and anxiety; while highlighting distinct avenues by which these behaviours can manifest. Limitations include subjects being within varying phases of lockdown during the time of testing and a large degree of study dropout (n=1,515). We emphasise the relevance of identifying those in need of greater support services to mitigate negative health outcomes associated with problematic alcohol consumption and internet usage in the context of COVID-19 isolation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".