Mental health experiences with COVID-19 public health measures in an Alberta First Nations Community
Bibliographic record
Abstract
BACKGROUND: First Nations (FN) people of Canada experience health, social, and systemic inequities due to colonization. Consequently, COVID-19 has placed further mental health stress on people related to personal finances, employment security and worry over infection, resulting in exacerbated effects of unresolved past medical and physical traumas. This study aims to understand the experiences related to mental health in an Alberta FN community during the early stages of the pandemic. METHODS: In partnership with FN leadership, the study implemented an online cross-sectional survey. Adults from a large FN community in Alberta, Canada, were asked to complete a survey, including two mental health-related screening questionnaires: (1) Generalized Anxiety Disorder-2 item; and (2) Patient Health Questionnaire-2 item. In addition, respondents could provide responses to open-ended questions about their experiences. RESULTS: Among 106 respondents, 95 (89.6%) finished the survey; 18% of adults screened positive for depressive symptoms (score of 3 or greater) and reported difficulty following public health advice for using hand sanitizer, maintaining social distancing, or self-isolating. 21% of adults screened positive for symptoms of anxiety (score of 3 or greater) and reported difficulty maintaining social distance, self-isolating, obtaining food and clothing, or meeting other basic living requirements. CONCLUSIONS: FN communities may be disproportionately affected by COVID-19, and may experience exacerbated symptoms of anxiety, depression and overall poor mental health and well-being. Additional supports and services, including for mental health, should be considered for FN in the context of COVID-19 public health measures. HIGHLIGHTS: The COVID-19 pandemic has brought upon increased stress and accompanying symptoms of anxiety and depression for a First Nations community in Alberta. Studies, such as this one, that characterize the influence of the COVID-19 pandemic on mental health among First Nations people, are urgently needed because of increasing demands on healthcare systems due to the pandemic and potential delays in the care of patients living with pre-existing mental health conditions. There is an opportunity to capitalize on First Nations people's experiences of post-traumatic growth proactively supporting/maintaining their well-being and possibly the development of community-based mental health interventions and supports.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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".