The impact of source and consumption of news on mental distress among inflammatory bowel disease patients during the COVID-19 pandemic
Bibliographic record
Abstract
Background: We sought to understand the trends in media use, and how consumption and source affected mental health of persons with inflammatory bowel disease during the early parts of the pandemic. Dissemination of news during the coronavirus disease 2019 (COVID-19) pandemic was integral to educating the public but also could be harmful if constantly consumed, leading to worsening anxiety. Methods: We performed a survey study in autumn 2020 during the second wave of COVID-19 in Manitoba. The survey included questions on consumption of COVID-19 news, along with validated measures of perceived stress, generalized anxiety, health anxiety, and depression. We used multivariable logistic regression analysis to assess trusted sources of news as a predictor of clinically significant mental health symptoms. Results: Of the 2940 participants in the registry, 1384 (47.1%) persons responded. The most trusted sources of news were television (64.2%), internet (46.1%), newspaper (27.6%), friends/family (21.7%), social media (16.9%), and radio (16.6%). Those who trusted social media had higher odds of depression (aOR 1.52, 95%CI 1.04-2.22), and perceived stress (aOR 2.56, 95%CI 1.09-2.21). Persons who reported extreme difficulty limiting their time-consuming news about COVID-19 and who spent more than 1 h daily consuming information on COVID-19 both had increased odds of any clinically significant mental health symptoms. Conclusions: It is unknown if consumption of COVID-19 news led to heightened mental health symptoms or if increasing anxieties and concerns led to consuming more news. Further research is needed to assess whether these elevated mental health symptoms led to worse disease outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".