Methods: A community-based participatory research method was utilized. Two focus group discussions (FGDs) were conducted in Malaysian sign language (BIM) with a total of 10 DHH individuals. Respondents were recruited using purposive sampling. Video-recordings were transcribed and analyzed using a thematic approach.
Results: Two themes emerged: (I) challenges and scepticism of the healthcare system; and (II) features of the mHealth app. Respondents expressed fears and concerns about accessing healthcare services, and stressed on the need for sign language interpreters. There were also concerns about data privacy and security. With regard to app features, the majority preferred videos instead of text to convey information about their disease and medication, due to their lower literacy levels.
Conclusions: For an mHealth app to be effective, app designers must ensure the app is individualised according to the cultural and linguistic diversity of the target audience. Pharmacists should also educate patients on the potential benefits of the app in terms of assisting patients with their medicine-taking.
Methods: The questionnaire contained items on the socio-demographic characteristics, medical condition, quality of life (QOL), nutritional status, functional capacity, and depression status. The forward and backward translation processes of the original English language version of the questionnaire were undertaken by three independent linguistic translators, while its content was validated by an expert team consisting of seven geriatricians, physicians, dietitian, and lecturers. The Malay version of the questionnaire was tested for face validity in 10 older adult patients over 65 years of age. The internal consistency reliability and construct validity were evaluated among 166 older adult patients (mean age, 71.0 years; 73.5% male). The questionnaire was administered through face-to-face interviews with the patients. Minor amendments were made after the content and face validity tests.
Results: The internal consistency reliability was good, as the Cronbach's alpha for most of the scales surpassed 0.70, ranging from 0.70 to 0.98, with only one exception (Mini Nutritional Assessment Short-Form, Cronbach's alpha=0.62). The factor loadings for all scales were satisfactory (>0.40), ranging from 0.45 to 0.90.
Conclusion: The Malay-version CGA showed evidence of satisfactory internal consistency reliability and construct validity in Malaysian geriatric patients.
BACKGROUND: Job satisfaction is a known predictor of nurse retention. Although there is a broad understanding of the factors that affect job satisfaction, little is known about how these vary between home and expatriate nurses working in countries which rely on a multicultural migrant workforce.
METHODS: A descriptive qualitative approach was taken, in which 26 semi-structured interviews were conducted with nurses selected from different nationalities, all of whom were working in Saudi Arabian hospitals. Eight participants were Saudi Arabian, six Filipino, four Indian, four South African, two Jordanian and two Malaysian.
FINDINGS: Five themes were identified that differentiated the perceptions of expatriates regarding their job satisfaction from those of the home nurses: separation from family, language and communication, fairness of remuneration, moving into the future and professionalism.
CONCLUSION: Focusing on the enhancement of job satisfaction experienced by expatriate nurses can result in a healthier work environment and greater retention of these nurses.
IMPLICATIONS FOR NURSING AND NURSING POLICY: To enhance nurse retention, policy makers in countries with migrant nurses should address their socio-economic needs. This includes providing both greater access to their dependent family members, and language lessons and cultural orientation to reduce linguistic and cultural challenges.
DESIGN: 1805 consecutive unselected patients with FGID who presented for primary or secondary care to 11 centres across Asia completed a cultural and linguistic adaptation of the Rome III Diagnostic Questionnaire that was translated to the local languages. Principal components factor analysis with varimax rotation was used to identify symptom clusters.
RESULTS: Nine symptom clusters were identified, consisting of two oesophageal factors (F6: globus, odynophagia and dysphagia; F9: chest pain and heartburn), two gastroduodenal factors (F5: bloating, fullness, belching and flatulence; F8 regurgitation, nausea and vomiting), three bowel factors (F2: abdominal pain and diarrhoea; F3: meal-related bowel symptoms; F7: upper abdominal pain and constipation) and two anorectal factors (F1: anorectal pain and constipation; F4: diarrhoea, urgency and incontinence).
CONCLUSION: We found that the broad categorisation used both in clinical practice and in the Rome system, that is, broad anatomical divisions, and certain diagnoses with long historical records, that is, IBS with diarrhoea, and chronic constipation, are still valid in our Asian societies. In addition, we found a bowel symptom cluster with meal trigger and a gas cluster that suggests a different emphasis in our populations. Future studies to compare a non-Asian cohort and to match to putative pathophysiology will help to verify our findings.
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.