Perceptions and Correlates of Distress Due to the COVID-19 Pandemic and Stress Management Strategies Among Adults With Diabetes: A Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: Greater risk of adverse health outcomes and public health measures have increased distress among people with diabetes during the coronavirus-2019 (COVID-19) pandemic. The objectives of this study were to explore how the experiences of people with diabetes during the COVID-19 pandemic differ according to sociodemographic characteristics and identify diabetes-related psychosocial correlates of COVID distress. METHODS: Patients with type 1 or 2 diabetes were recruited from clinics and community health centres in Toronto, Ontario, as well as patient networks. Participants were interviewed to explore the experiences of people with diabetes with varied sociodemographic and clinical identities, with respect to wellness (physical, emotional, social, financial, occupational), level of stress and management strategies. Multiple linear regression was used to assess the relationships between diabetes distress, diabetes self-efficacy and resilient coping with COVID distress. RESULTS: Interviews revealed that specific aspects of psychosocial wellness affected by the pandemic, and stress and illness management strategies utilized by people with diabetes differed based on socioeconomic status, gender, type of diabetes and race. Resilient coping (β=-0.0517; 95% confidence interval [CI], -0.0918 to -0.0116; p=0.012), diabetes distress (β=0.0260; 95% CI, 0.0149 to 0.0371; p<0.0001) and diabetes self-efficacy (β=-0.0184; 95% CI, -0.0316 to -0.0052; p=0.007) were significantly associated with COVID distress. CONCLUSIONS: Certain subgroups of people with diabetes have experienced a disproportionate amount of COVID distress. Assessing correlates of COVID distress among people with diabetes will help inform interventions such as diabetes self-management education to address the psychosocial distress caused by the pandemic.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".