Materials and methods: A systematic literature survey was adopted in this paper, involving the review of 25 relevant researched articles found in the databases Science Direct, EBSCO, MEDLINE, CINAHL and PubMed.
Result: The systematic literature survey reveals five variables to be core predictors of TQM, signifying how important these variables are in the successful implementation of TQM in the health-care context. Also, it is revealed that the identified core predictors have positive effects on an improved health-care system. However, the systematic survey of the literature reveals a dearth of studies on TQM in the health-care context.
Conclusion: As TQM has become an important management approach for advancing effectiveness in the health-care sector, this kind of research is of value to researchers and managers. Stakeholders in the health sectors should introduce and implement TQM in hospitals and clinics. Nevertheless, this study has limitations, including that the databases and search engines adopted for the literature search are not exhaustive.
METHOD: We conducted a cross-sectional analysis to compare ChatGPT, Google Bard, and medical students in mass casualty incident (MCI) triage using the Simple Triage And Rapid Treatment (START) method. A validated questionnaire with 15 diverse MCI scenarios was used to assess triage accuracy and content analysis in four categories: "Walking wounded," "Respiration," "Perfusion," and "Mental Status." Statistical analysis compared the results.
RESULT: Google Bard demonstrated a notably higher accuracy of 60%, while ChatGPT achieved an accuracy of 26.67% (p = 0.002). Comparatively, medical students performed at an accuracy rate of 64.3% in a previous study. However, there was no significant difference observed between Google Bard and medical students (p = 0.211). Qualitative content analysis of 'walking-wounded', 'respiration', 'perfusion', and 'mental status' indicated that Google Bard outperformed ChatGPT.
CONCLUSION: Google Bard was found to be superior to ChatGPT in correctly performing mass casualty incident triage. Google Bard achieved an accuracy of 60%, while chatGPT only achieved an accuracy of 26.67%. This difference was statistically significant (p = 0.002).
Purpose: The purpose of this study was to identify the knowledge, attitude, and barriers towards the implementation of EBP among physiotherapists in Malaysia.
Methods: A survey was conducted among the members of the Malaysian Physiotherapy Association and other practicing therapists in Malaysia. One hundred and two responses were collected throughout a span of 2 months.
Results: Respondents agreed that EBP is necessary to practice and that it helps in decision making as well as improving patient care. Eighty-one percent of the respondents either agreed or strongly agreed that they had received formal training in EBP. However, 61% of the respondents reported that strong evidence is lacking to support their interventions. Thirty percent of the respondents reported reading <2 articles per month, with 57% stating that they read two to five articles in a typical month. This study also found time constraints, limited access to search engines, and lack of generalizability of research evidence as the top three barriers to implementing EBP.
Conclusion: Physiotherapists in Malaysia had a positive attitude towards EBP and are inclined towards implementing evidence into their clinical practice. They are interested in attending courses to improve their knowledge and skills in EBP.