COVID-19 and its effects on social connectedness among Malaysian Malay living abroad
Bibliographic record
Abstract
Ever since the global pandemic COVID-19 emerged, governments all around the world have attempted to slow the spread of this disease by promoting social isolation and social distancing. Although physical separation does curtail the spread of the virus, the practice of social distancing has limited people’s in-person social interactions and has narrowed their sense of social connectedness. To Malaysian Malay expatriates, social connectedness is more than just a means of social relationship or social networking. Social connectedness is a motivating factor for survival and a way to reduce feelings of social anxiety and frustrations when living in a foreign host country. To highlight the significance of social connectedness while working abroad, this study was conducted with 11 single Malaysian Malays residing in the United States of America, United Kingdom, Australia, and Canada. A qualitative approach was employed in this study by using in-depth interviews to examine the socio-cultural challenges they experienced while working and living in a country different from their own. This paper discusses how COVID-19 affects Malaysian Malay expatriates’ social connectedness while living abroad in host countries and the need for more research exploration in the subject area. As a result, although social media can be a platform for everyone to be connected, face-to-face interactions are more desirable. Furthermore, the researchers also found that practising a level of intimacy with close friends can help Malaysian Malay expatriates to gain social connectedness with others which also leads to the feeling of belongingness in their community.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".