Adaptation in communication technology utilization: caring for individuals with chronic conditions in South Asia during the Covid-19 pandemic
Bibliographic record
Abstract
BACKGROUND: During the Covid-19 pandemic, people with chronic conditions experienced delayed or missed care, while their carers endured social isolation, loneliness, and reduced support. Information communication technology (ICT) can be utilized to encourage continuity of care, address misinformation, and allocate support. This study aimed to identify factors associated with the ICT adaptation of South Asian carers of individuals with chronic conditions by comparing changes in ICT utilization and preferences before and during the pandemic. METHOD: 416 South Asian carers reporting feelings of loneliness and isolation were identified from the Coping with Loneliness, Isolation and Covid-19 (CLIC) online survey. Descriptive statistics and multinomial regression models were utilized. RESULT: The most commonly used ICT modality was auditory, followed by written and audio-visual. Four variables identified were: social network size and relationship proximity, Covid-19-induced distress, age, and living arrangements. We identified a negative correlation between social network size and ICT frequency/intensity, reductions in communication frequency/intensity associated with Covid-19-induced distress, working-age carer (18-60) preference adaptation toward written communication during the pandemic, written and auditory ICT fluency in carers spending time alone by choice, and aversion from auditory ICT in carers who lived and were often alone involuntarily. CONCLUSION: The findings provide insights into South Asian carers' ICT usage, preferences, and adaptation in response to the pandemic. The findings aid in the development of health and social care pathways that fulfil local caregivers' unmet support and resource needs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".