Cultivating resilience among Hong Kong's underprivileged ethnic minority groups in the face of a pandemic through a social justice lens
Bibliographic record
Abstract
Purpose In this study, the authors investigated ways to cultivate resilience through a social justice lens among ethnic minorities against COVID-19 in Hong Kong. Design/methodology/approach A qualitative (case study) methodology was adopted to interview 15 social service providers from diverse ethnic backgrounds serving disadvantaged ethnic minority groups (South and Southeast Asian groups from low-income households, foreign domestic workers and asylum seekers/refugees). Findings Two major protective factors were identified, contributing to the development of resilience among diverse ethnic groups: (1) individual-based resilience (including being optimistic) and (2) socio-environmental factors (including ongoing support from strong family, peer and religious settings' support, consulates' support, on-going material and nonmaterial donations, support of young volunteers and importance of online connection and communication) using the integration of resilience and social justice frameworks. Originality/value This study showed that the protective factors were found to dynamically interact with each other and the environment. The present study recommends additional culturally sensitive service and policy implications for preventing the long-term impact of mass crises among Hong Kong's marginalized minorities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".