Evaluating the Prevalence and Predictors of Moderate to Severe Depression in Fort McMurray, Canada during the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: The Coronavirus disease (COVID-19) pandemic has produced adverse health consequences, including mental health consequences. Studies indicate that residents of Fort McMurray, a community which has experienced trauma from flooding and wildfires in the past, may be more vulnerable to the mental health effects of the pandemic. OBJECTIVE: This study aimed to examine the prevalence and predictors of likely Major Depressive Disorder (MDD) among residents of Fort McMurray during the COVID-19 pandemic. METHODS: A cross-sectional approach was adopted utilizing an online survey questionnaire to gather sociodemographic data, COVID-19 related data, and clinical information, including likely MDD using the Patient Health Questionnaire (PHQ-9) scale, from the residents of Fort McMurray between the period of 24 April to 2 June 2021. RESULTS: Overall, 186 individuals completed the survey out of 249 residents who accessed the online survey, yielding a completion rate of 74.7%. The prevalence of likely MDD among respondents was 45%. Respondents willing to receive mental health counselling were five times more likely to experience MDD during the COVID-19 pandemic (OR = 5.48; 95% CI: 1.95-15.40). Respondents with a history of depression were nearly five folds more likely to report MDD during the era of the pandemic than residents without a history of depression (OR = 4.64; 95% CI: 1.49-14.44). Similarly, respondents with a history of taking hypnotics (sleeping tablets) were nearly six-fold more likely to express MDD than respondents with no history of receiving sleeping tablets (OR = 5.72; 95% CI: 1.08-30.30). Finally, respondents who reported receiving only partial support from the employer had three times higher odds of having likely MDD than those who received absolute support from the employer (OR = 3.50; 95% CI: 1.24-9.82). CONCLUSION: In addition to the effect of the pandemic and other measures taken to curb the psychopathological impact of the pandemic, policymakers need to implement policies to manage individuals with preexisting mental health conditions and provide strong employer support.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".