This paper aimed to measure the vulnerability type that predicts the likelihood of the respondents in poor category of household income by sex-disaggregated data. The respondents were sampled in Peninsular Malaysia (n=322)). They suffer from at least one out of six types of vulnerability asked in the questionnaire. HO1 (no type of vulnerability predicts male in poor category of household income), and HO2 (no type of vulnerability predicts male in poor category of household income) were tested through Binary Logistic Regression (BLR) Model respectively and rejected. Both models were fit and significant (p<0.05) to predict the male and female respondents in poor category of household income. BLR Model 1 (male respondents) significantly predicted Handicapped Male and Single Father, with 3.60 times and less than 93.9 percent of the likelihood respectively for them to be in poor category of household income. For BLR Model 2 (female respondents), Single Mother and Handicapped Women predicted 16.15 times and 72.5 percent less of the likelihood respectively for them in the poor category of household income. This paper concludes the vulnerable women are poorer than the vulnerable men, and the handicapped men and single mothers are the poorest in freshwater fisheries communities.

This paper aims to identify the socio-economic determinants significantly predict poverty status of older respondents by sex disaggregation. A total of n=172 respondents reported, and four Hos tested through Binary Logistic Regression Model 1-4, respectively. All Hos were rejected because all models fit and significant (p<0.05). Through HO1 and HO2 testing respectively among male respondents, two predictors obtained -working status and district. In Model 1 and 2, working status predicts less than 88.6 percent and 8.784 times likelihood the respondents were in non-poor and poor category respectively. In Model 1, Miri Sibu, and Betong districts had significantly (p<0.05) predict 9.439 times, 51.352-, and 26.402-time likelihood the respondents were in non-poor category. Whereas in Model 2, Miri, Sibu, and Betong districts had significantly (p<0.05) predict less than 89.4 percent, 98.1 percent, and 96.2 percent likelihood the respondents were in poor category, respectively. Through HO3 and HO4 test respectively among female respondents, two predictors were obtained -strata and current transfer. Rural strata predict less than 79.1 percent (Model 3) and 4.789 likelihood (Model 4) the respondents were in non-poor and poor category respectively. Current transfer predicts less than 99.1 percent (Model 3) and 113.44-time (Model 4) likelihood the respondents were in non-poor and poor category respectively.

This paper aimed to measure the variable that predicts the likelihood of female respondents to wear face mask. The data were collected (n=501) through online survey using Google Form. The set of questionnaire included the respondents' background, knowledge about COVID-19, the awareness on COVID-19, and behaviour related to COVID-19 protection especially in wearing a face mask, washing hands, and social distance. Respondents in this study comprised the majority of females, Malay ethnic and from urban areas, at average age=65.55 years old. Majority (75.25%) reported that they are unemployed, married (78.24%), and had tertiary education (74.85%). Knowledge and awareness on COVID-19 significantly predict respondents to wear face mask. High knowledge predicts 19.194 times the likelihood of respondents to wear face mask, and high awareness predicts that less than 95.6% of respondents to wear face mask. Nevertheless, no variable predicts male respondents to wear face mask, and only high knowledge in COVID-19 predicts 41.340 times the likelihood of female respondents to wear a face mask. As conclusion, only high knowledge of COVID-19 predicts the behaviour of wearing face mask. Thus, effort should be focused on providing the public with good knowledge of COVID-19 to sustain good behaviour with regard to COVID-19 protection.