Prevalence and Correlates of Anxiety in Fort McMurray Vulnerable Population during the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction The COVID-19 pandemic has produced negative mental health outcomes, which were more prominent in vulnerable communities, such as Fort McMurray (FMM), the community that experienced prior similar disasters. Objectives This study aimed to examine the likelihood and correlates of anxiety symptoms among FMM residents, during the COVID-19 pandemic. Methods A cross-sectional online survey questionnaire was applied between 24 April and 2 June 2021, at FMM community to gather a set of data, including sociodemographic, COVID-19, and clinical information. Generalized anxiety disorder was the main outcome of the study, and was measured using GAD-7 scale. Results Overall, 186 individuals completed the survey (response rate 74.7%). Most of the respondents were females (159, 85.5%); above 40 years (98, 52.7%); employed (175, 94.1%); and in relationship (132, 71%). The prevalence of moderate-to-severe anxiety was (42.5%, 71) on GAD-7 self-reported scale. Subscribers who reported that they would like to receive mental health support; have received no family support since COVID-19 declaration; and have lost their job during the pandemic were all more likely to report moderate-to-severe anxiety (OR = 3.39; 95% CI: 1.29-8.88), (OR = 4.85; 95% CI: 1.56-15.03), and (OR = 4.40; 95% CI: 1.01-19.24), respectively. Conclusions Anxiety levels were high among FMM residents, compared to levels before COVID-19. Clinical and social factors related to the COVID-19 pandemic significantly predicted likely anxiety among Fort McMurray population. It is imperative to mobilize resources to support vulnerable communities during the COVID-19 pandemic. Disclosure of Interest None Declared
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".