Impact of Social Isolation and Digital Divide on Mental Health and Wellbeing in Patients with Mental Health Disorders during COVID-19: A Multiple Case Study
Bibliographic record
Abstract
The positive relationship between social connections and mental health and wellbeing has been widely documented. During the initial stage of the pandemic, COVID-19 associated restrictions had given rise to social isolation that had a negative effect on individuals' mental health and wellbeing, particularly among patients with preexisting mental health disorders. To abridge physical distance, digital technology had become a primary method of communication and social engagement. However, not everyone had access to internet and devices required to connect online due to the digital divide, especially among marginalized populations. The purpose of this multiple case study was to explore experiences of social isolation and the digital divide among patients with mental health disorders, and its impact on their mental health and wellbeing. Our findings revealed that social isolation was the major contributing factor to the intensification of mental health symptoms, while the digital divide (e.g., financial constraints and low proficiency in digital technology) was recognized as a barrier to making social connections via digital technologies. Nurses should engage with communities and policymakers in developing strategies to address the social determinants of health disparities during the current pandemic, other disruptive pandemics and beyond.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".