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Background Chronic diseases are known to detrimentally impact an individual’s quality of life (QOL) and well-being. Therefore, this study aims to evaluate the QOL and overall well-being among both healthy individuals and those with diverse primary diagnoses. Methods This is a cross-sectional study and data collection took place from May 2022 to May 2023. Information regarding healthy participants was gathered from healthcare workers without any comorbidities. Data for non-healthy participants were collected from individuals diagnosed with various conditions across four specialist clinics: nephrology, oncology, psychiatry, and cardiology. All participants completed the Significant Quality of Life Measures (SigQOLM), a comprehensive assessment tool consisting of 69 items that evaluate 18 domains of QOL and well-being. Results The study included a total of 452 participants, with 284 (62.8%) classified as healthy. Among the non-healthy participants, 41 (9.1%) had end-stage renal diseases (ESRD), 48 (10.6%) were diagnosed with cancer, 40 (8.8%) had depressive disorder, and the remaining had heart disease (8.6%). Statistical analysis revealed significant differences ( p  < 0.001) between healthy and non-healthy participants in both overall SigQOLM scores and across all 18 domains of SigQOLM. Conclusion Generally, healthy participants also experienced excellent QOL and well-being. However, disparities in both QOL and overall well-being were evident among patients with various diagnoses. These findings provide valuable insights for medical practitioners and policy makers by enabling them to tailor interventions to enhance the QOL and well-being of their patients.

Objective The “Health” element is one of the elements in Significant Quality of Life Measure (SigQOLM) that measures quality of life and well-being of people. This study aims to evaluate the Health element (Health-SigQOLM) as a generic and dynamic scale to measure health-related quality of life (HRQOL) with a broader spectrum of coverage. This study used a secondary data that developed SigQOLM. Only the “Health” element with 33 items is used for analysis. Results The construct of Health-SigQOLM has a minimum factor loading of 0.425 with excellent model fit. The health status among healthcare workers is significantly associated with the Health-SigQOLM ( p  < 0.001). The Health-SigQOLM score can clearly distinguish between healthy people and those who have been afflicted with some diseases but have never been hospitalized due to disease progression or other associated complications ( p  = 0.002). The Health-SigQOLM is a generic and dynamic tool for assessing various aspects of health-related quality of life.

Objectives Health-Significant Quality of Life Measure (Health-SigQOLM) provides a generic and dynamic assessment of Health-related quality of life (HRQOL). This study aims to assess the HRQOL among healthy and non-healthy participants with varying chronic diseases. Results Comparisons between healthy and non-healthy participants revealed statistically significant differences ( p  < 0.001) in the mean overall HRQOL score as well as across all its nine domains. Therefore, the Health-SigQOLM, along with its nine domains, is demonstrated to have adequate sensitivity in distinguishing between healthy and non-healthy study participants. This had supported the evidence that the Health-SigQOLM is a reliable and valid scale for measuring both generic and dynamic HRQOL.

Health-related quality of life (HRQOL) is one of the most important outcome measures to be assessed by medical research. This study aims to develop and validate an instrument called the “health-related quality of life with six domains” (HRQ-6D), which aims to measure an individual’s health-related quality of life within a 24 h period of time. This is a questionnaire development study involving five phases, which are (i) to explore the subject matter content for gaining a better understanding of the topic, (ii) to develop the questionnaire, (iii) to assess both its content validity and face validity, (iv) to conduct a pilot study, and finally, (v) to undertake a field testing of the questionnaire. For the field-testing phase, a cross-sectional study involving a self-administered survey for HRQ-6D items was conducted among healthcare workers with various health conditions. Exploratory factor analysis was initially applied to construct the major dimensions of the HRQ-6D. Confirmatory factor analysis was subsequently applied to evaluate the model fit of the overall framework of the HRQ-6D. The clinical utility of this HRQ-6D was also assessed via its association with actual clinical evidence. A total of 406 respondents participated in the survey. Six domains were identified from the analysis, namely “pain”, “physical strength”, “emotion”, “self-care”, “mobility”, and “perception of future health” comprising two items in each domain. Each domain was reported to have a minimum value of Cronbach’s alpha of 0.731, and the model fit for the overall framework of the HRQ-6D was also found to be excellent. Exploratory factor analysis was undertaken for the 12 items of the HRQ-6D. All the domains can be categorized into three major dimensions, namely “health”, “body function”, and “future perception”, with a minimum value for their factor loadings of at least 0.507. A notable finding was that the HRQ-6D was significantly associated with an individual’s existing comorbidities and current status of health (p < 0.05). This study successfully validated the HRQ-6D, and we found it to possess both excellent levels of reliability and validity and a satisfactory degree of model fit; it was also significantly associated with actual clinical evidence.

Background/Objectives: This study aims to compare the health-related quality of life (HRQOL) between healthcare providers without chronic diseases and participants with chronic diseases presenting with one of the four different primary diagnoses on the health-related quality of life with six domains (HRQ-6D) scale. Methods: This is a cross-sectional study to compare the HRQOL between healthcare providers without chronic diseases and participants with chronic diseases. Data collection was performed from May 2022 to May 2023. Data for the comparison group were taken from healthcare providers without chronic diseases, and for the participant group with chronic diseases, the data were collected from actual patients with one of four types of primary diagnoses who were recruited from specialist cardiology, oncology, psychiatry, and nephrology clinics. All the participants of this study filled in the HRQ-6D. Results: There were 238 (58.6%) healthcare providers without chronic diseases who participated in this study, as well as 41 (10.1%) patients with end-stage renal disease (ESRD), 48 (11.8%) patients with cancer, and 40 (9.9%) patients who were depressed, and the remaining patients had heart disease. The means (SD) of HRQ-6D scores among healthcare providers without chronic diseases for pain, physical strength, emotion, mobility, self-care, perception of future health, and overall HRQ-6D score were 75.3% (19.8), 74.5% (21.1), 85.6% (18.4%), 93.0% (12.3), 91.6% (13.9), 74.2% (23.3), and 82.4% (13.6), respectively. In comparisons between healthcare providers without chronic diseases and participants with chronic diseases, all mean differences of the overall HRQ-6D score and its domains and dimensions were statistically significant (p < 0.001). Conclusions: The overall score of the HRQ-6D, as well as its domains and dimensions are sensitive in detecting the study participants with chronic diseases from among those without chronic diseases. Therefore, the HRQ-6D is a reliable and valid scale to measure HRQOL. Future studies may use this scale for interventional, observational, and cost-effectiveness studies.

Background/Objectives: The HRQ-6D is a newly developed instrument to measure Health-related quality of life (HRQOL) and EQ-5D is the gold standard for measuring HRQOL. This study aims to test the concurrent validity between EQ-5D and HRQ-6D measures among patients with different primary diagnoses. Methods: This cross-sectional study uses two HRQOL measurement instruments, EQ-5D-3L and HRQ-6D. Data collection was performed between January 2023 and May 2023. All the necessary data for this study were collected from actual patients who presented with any one of the four different types of primary diagnoses: heart disease, cancer, depressive disorders, and end-stage renal disease (ESRD). They were recruited from the four specialist clinics that cater to the treatment of each of the four different types of primary diagnoses in a tertiary hospital. Results: There were 149 patients who participated in the study wherein 40 (26.8%) of them were ESRD patients, 39 (26.2%) of them were cancer patients, 38 (25.5%) of them were mentally depressed, and the remaining were patients with heart diseases. The domains in HRQ-6D, except for the perception of future health, are significantly associated with domains in EQ-5D-3L after having controlled for patients’ primary diagnoses (i.e., p < 0.001). The HRQ-6D replaces the domain “Usual activities” with “Physical energy,” and the association between these two domains is significant (p < 0.001). The correlation between the overall HRQ-6D and EQ-VAS scores is also significant (coefficient = 0.445, p < 0.001). Conclusions: The HRQ-6D is demonstrated to have concurrent validity with EQ-5D. Therefore, clinicians and researchers can use HRQ-6D to measure patient outcomes for interventional and observational studies. (Total word count = 265 words).

Background: This study aims to develop and validate a job satisfaction questionnaire (JS-Q) for health workforce who are employed by a healthcare institution. Methods: The study consists of six phases which begins with eliciting a conceptual understanding of the subject matter which is then followed by questions development, designing the overall structure and format of the questionnaire, assessing both its content validity and face validity, conducting a pilot study and finally a field test. A sample of study respondents who were permanent hospital staff above 18 years of age had been recruited from three government hospitals in Kuching, Sarawak, Malaysia. Results: The finalised JS-Q consists of a total of 34 questions that were based on 8 domains. For all these 8 domains, the minimum loading of each item on the factors was calculated to be at least 0.500, its coefficient of Cronbach’s alpha was calculated to be at least 0.750 and its corrected item-total correlation was calculated to be at least 0.500. The goodness of fit of the model was determined to be satisfactory with a value of Chi-square/df < 3.0, and a value of root mean square error approximation (RMSEA) < 0.8 and finally with both Tucker Lewis index (TLI) and comparative fit index (CFI) > 0.9. Conclusion: This newly developed and validated questionnaire (JS-Q) is found to be a valid and reliable study instrument for assessing job satisfaction among health workforce.