A Qualitative Study about the Psychosocial Issues of COVID-19 Perceived by the South Asian Bangladeshi Senior Immigrants Living in Toronto, Ontario
Bibliographic record
Abstract
Background: People stayed home and got isolated during the pandemic time (COVID-19). The pandemic passed more than a year, and it is still ongoing. There is not enough information about the psychological and social issues of the COVID-19 on the South Asian senior immigrants living in Toronto. Aim: The study aimed to explore the description of COVID-19 from the experience of the South Asian seniors and to understand the perceived psychosocial issues of COVID-19 on them. It helps policymakers develop adequate policies and initiatives for the South Asian Bangladeshi senior immigrants during and after the pandemic. Methods: The study applied open-ended questions for the phone interview with 52 seniors (>55 years). It used thematic analysis for the interpretation of qualitative data. Each interview took 45-60 minutes to complete. Results: The seniors described COVID-19 in medical, mental, and social aspects. They described COVID-19 as ‘viral and pandemic infections,’ ‘health problems,’ ‘lack of treatment,’ and ‘death.’ They also described COVID-19 as ‘worrying,’ ‘dangerous,’ ‘isolated society,’ ‘lack of recreation,’ ‘staying home like a prison,’ and ‘shut down everywhere.’ Many seniors felt lonely as the pandemic disconnected them from the family members and the outdoor activities. They were also scared to get infected, were worried about seeing deaths and the shortage of vaccines worldwide and were sad as they could not meet people in person. Many seniors stayed home for months. They could not go outside for worship, doctors, shopping malls, and they felt that they had an unusual lifestyle. Conclusion: Based on findings, adequate information, mental health supports, and virtual programs are needed to address the psychological and social issues of COVID-19.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".