The Role of Communications in the Resilience of Risk Group in Kuching, Sarawak, During The Dispersal of COVID-19 Virus
Bibliographic record
Abstract
The Covid-19 Pandemic has significantly interfered with all aspects of human life. Notably, this dangerous epidemic has a more significant impact on the risk group. It has caused them to feel stress, fear, and suffering from the ripple effect of Covid-19. Hence, this disruption occurred instantaneously, requiring people to demonstrate noteworthy adaptability and resiliency through communication. Therefore, this study examined the two dimensions of communication, namely self-centred communication (knowledge and optimistic thinking) and external communication (family support and media exposure), in predicting the resilience among the risk group in Kuching, Sarawak. The data collection was during the movement control order, so the distribution of the online questionnaire used voluntary response sampling to the target respondents through WhatsApp and social media accounts. Data collected was analysed using Partial Least Squares analyses. The results show that knowledge, optimistic thinking, and family support significantly correlate with the resilience of the risk group. Moreover, the self-centred communication constructs were the best predictor for resilience. However, media exposure was insignificant because they were in survival mode when it threatened the risk group's lives. So, this makes them prone to rely more on their knowledge, optimistic thinking, and their own family to help their resilience. Hence this has made them think the role of media is less important in resilience. This study has managed to contribute some useful suggestions for helping risk groups protect themselves during a pandemic. Critically, these findings can update relevant authorities in designing effective interventions to support risk groups by strengthening communication factors associated with resilience. Keywords: Risk group, chronic illness, COVID-19 pandemic, resilience, communication.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".