Background: A great deal of research has been carried out on the assessment of the eudaimonic perspective of psychological well-being and the hedonic perspective of subjective well-being. The Flourishing Scale (FS) has been extensively used in research and practice, as it assesses the fundamental aspects of social psychological functioning. Nevertheless, the psychometric properties of Urdu versions of eudaimonic measures, such as the FS, have not yet been ascertained. The translation and validation of the FS in the Urdu language was not available, and hence this study was planned with the aim to validate the Urdu version of the FS. Methods: We assessed the psychometric properties of the FS in a sample of adults aged 18 years and above in Pakistan (N = 130) using exploratory factor analysis based on principal component analysis with varimax rotation and confirmatory factor analysis. Results: The exploratory factor analysis confirmed the unidimensional nature of the 8-item FS. We assessed that the Urdu version of the FS showed a high internal consistency reliability (α = 0.914) with a significant intraclass correlation coefficient (ICC), p < 0.001). In our study, the Kaiser-Mayer-Olkin value was 0.915 with a chi-square test value (χ2) of 637.687, and Bartlett's test of sphericity was significant (df = 28, p < 0.001). The intraclass correlation coefficients (ICCs) at test-retest for all domains were statistically significant (p < 0.001) and showed excellent agreement for all the items. The revised confirmatory factor analysis revealed a good-fit model, but with item 8-"People respect me"-removed due to its lower factor loading. Conclusions: The findings suggest that the FS is a psychometrically sound instrument for assessing social psychological functioning among adults in Pakistan. Therefore, the validated Urdu version of the FS may be used in future studies of well-being in clinical psychology and positive psychology.
The Kalasha are a religious, ethnic, and linguistic minority community in Pakistan. They are indigenous people living in remote valleys of the Hindu Kush Mountains in northern Pakistan, neighboring Afghanistan. The Kalasha are pastoral, as well as agricultural people to some extent, although they are increasingly facing pressures from globalization and social change, which may be influencing youth and community development. Their traditional world view dichotomizes and emphasizes on the division of the pure (Onjeshta) and the impure (Pragata). There remains a scarcity of literature on mental health and resilience of indigenous communities in South Asia and Pakistan generally, and the polytheistic Kalasha community specifically. Thus, the current study was conducted with the aim to explore the cultural protective factors (resilience) of the Kalasha youth (adolescents and emerging adults) and to explore their perceived etiological understandings and preferred interventions for mental health support systems. The theoretical framework of Bronfenbrenner's (1, 2) ecological systems model was used. Interpretative Phenomenological Analysis (IPA) was conducted, considering the advantage of its idiographic approach and the "double hermeneutic" analytic process. This methodology was consistent with the aim to understand and make sense of mental health and resilience from the Kalasha indigenous perspective. A total of 12 in-depth interviews were conducted with adolescents and emerging adults (5 males, 7 females), along with ethnographic observations. The analysis revealed 3 superordinate themes of mental health perceptions and interventions, each with more specific emergent themes: (1) Psychological Resilience/Cultural Protective Factors Buffering Against Mental Health Problems (Intra-Communal Bonding & Sharing; Kalasha Festivals & Traditions; Purity Concept; Behavioral Practice of Happiness and Cognitive Patterns); (2) Perceived Causes of Mental Health Issues (Biological & Psychosocial; Supernatural & Spiritual; Environmental); and (3) Preferred Interventions [Shamanic Treatment; Ta'awiz (Amulets); Communal Sharing & Problem Solving; Medical Treatment; Herbal Methods]. The overall findings point to the need for developing culturally-sensitive and indigenous measures and therapeutic interventions. The findings highlighted the Kalasha cultural practices which may promote resilience. The findings also call for indigenous sources of knowledge to be considered when collaboratively designing public health programs.
Background: This paper aimed to review the literature on the factors associated with parenting stress and resilience among parents of children with autism spectrum disorder (ASD) in the South East Asia (SEA) region. Methods: An extensive search of articles in multiple online databases (PsycNET, ProQuest, PudMed, EMBASE, CINAHL, Web of Science, and Google Scholar) resulted in 28 papers that met the inclusion criteria (i.e., conducted in the SEA region, specific to ASD only, published in a peer-reviewed journal, full text in English). Studies found were conducted in the following countries: Brunei, n = 1; Indonesia, n = 2; Malaysia, n = 12; Philippines, n = 5; Singapore, n = 5, Thailand, n = 2; and Vietnam, n = 1, but none from Cambodia, East Timor, Laos, and Myanmar were identified. Results: Across the studies, six main factors were found to be associated with parenting stress: social support, severity of autism symptoms, financial difficulty, parents' perception and understanding toward ASD, parents' anxiety and worries about their child's future, and religious beliefs. These six factors could also be categorized as either a source of parenting stress or a coping strategy/resilience mechanism that may attenuate parenting stress. Conclusion: The findings suggest that greater support services in Western countries may underlie the cultural differences observed in the SEA region. Limitations in the current review were identified. The limited number of studies yielded from the search suggests a need for expanded research on ASD and parenting stress, coping, and resilience in the SEA region especially in Cambodia, East Timor, Laos, and Myanmar. The identified stress and resilience factors may serve as sociocultural markers for clinicians, psychologists, and other professionals to consider when supporting parents of children with ASD.
BACKGROUND: The manual diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls. RESULTS: Our models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM). CONCLUSIONS: Experimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD.
Algorithms , Alzheimer Disease/diagnosis , Machine Learning , Speech , Aged , Aged, 80 and over , Alzheimer Disease/psychology , Biomarkers/analysis , Humans , Linguistics , Middle Aged
