A comparison of the mental health impacts and resilience of healthcare workers in rural Manitoba during the COVID-19 pandemic
Bibliographic record
Abstract
Drawing on resilience research in rural studies, this study examined the mental health experiences of frontline healthcare workers during two stages of the COVID-19 pandemic in rural Manitoba, Canada. Data were collected using online surveys from May to June 2020 (n = 137) and from May to June 2021 (n = 219). The surveys assessed symptoms of anxiety and identified strategies and barriers to addressing mental health concerns. Most respondents exhibited clinical symptoms of anxiety as measured by the GAD-7 scale. Respondents mostly accessed informal supports, such as family and friends, in the first survey in 2020, and a broader mix of informal and formal supports in the 2021 survey. In both surveys, numerous barriers to accessing formal mental health support were identified. Our findings suggest that although some degree of resilience in the face of the pandemic was prevalent in rural areas, there is a need for accessible professional and peer mental health support for frontline healthcare workers. The research also highlights the importance of context and resources in sustaining the healthcare workforce in rural areas as part of the pandemic response. • The COVID-19 pandemic directly impacted frontline health care workers. • Individuals surveyed showed resilience in the face of unprecedented pressures in the workplace. • Frontline health care workers in rural areas faced unique barriers to mental health services. • Frontline health care workers in rural areas adapted unique strategies to deal with stress. • The resilience model describes resilience in the face of external forces, including facing barriers and adapting strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".