The Association Between Increased Maladaptive Health Behaviours and Elevated Mental Health Symptoms Among Persons with IBD During the COVID-19 Pandemic
Bibliographic record
Abstract
Aim: To assess the association between maladaptive health behaviours and elevated mental health (MH) symptoms during the COVID-19 pandemic among persons with inflammatory bowel disease (IBD). Methods: = 2,942) were invited to participate in a survey in November 2020, regarding their experiences during the COVID-19 pandemic. Maladaptive health behaviours included increased use of alcohol, marijuana, and cigarettes, and reduced exercise relative to pre-pandemic levels. Clinically significant MH symptoms were defined by the presence of elevated anxiety, depression, and/or post-traumatic stress. Adjusted logistic regression assessed the odds of elevated MH symptoms predicted by maladaptive health behaviours, stratified by gender. Results: Of 1,363 (46%) respondents, 319 (23%) had elevated MH symptoms. Those with elevated MH symptoms were older (mean age 54) and predominantly females (70%). The odds of any elevated MH symptoms were approximately two to four times greater among those who experienced maladaptive health behaviours during the pandemic including: increased alcohol use [aOR 2.14, 95% CI (1.50-3.05)], males who increased marijuana use [aOR 4.18, 95% CI (1.18-14.74)], females who increased smoking cigarettes [aOR 3.68 95% CI (1.15-11.86)] and any maladaptive health behaviour [aOR 1.93 95% CI (1.44-2.60)]. Conclusion: During the COVID-19 pandemic, persons with IBD who experienced any maladaptive health behaviour was associated with double the likelihood of experiencing clinically significant MH symptoms. For persons with elevated MH symptoms, it is important for health care providers to recognize the association of increased maladaptive behaviours. Alternatively, if it is determined that MH symptoms predated maladaptive health behaviours then, inquiries into MH and providing appropriate referrals should be pursued.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".