Singaporean Families’ Adaptation and Resilience During the COVID-19 Global Pandemic
Bibliographic record
Abstract
Drawing on governmental statements, press releases and major news reports on COVID-19 related measures, support and social issues, we illustrate and examine the challenges families are facing in Singapore during the pandemic. Employing a dual approach, we illustrate the extent of various institutional support and resources for families offered by the government in tandem with social distancing measures to restrict social activities, and closure of non-essential business, and also document issues related to the economy, education, family interactions and mental well-being of families from different social backgrounds. This approach enables us to show the extent to which Singaporean families have adapted to the different economic and social stressors and how institutional support has been utilized as a catalyst to resilience. The pandemic as a force of social change demands urgent research on the social impact on and resilience of families in Singapore. Future research directions should include children’s development with the interaction of family socioeconomic conditions, focus on mental well-being of all generations in the family, continuous safeguard of victims of domestic violence, empowerment, and investigations of the shifting family values. A concerted research strategy will offer the opportunity for a better understanding of the paradigm shift experienced by Singaporean families, and, to identify policy implications on strengthening the resilience of families.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".