1.Development and Validation of Clinical Nursing Teacher Self-Efficacy Scale and Investigation of Self-Efficacy among Clinical Nursing Teachers
Pei-Ling WU ; Ying-Chen TSENG ; Li-Chiu CHEN ; Shao-Mei TSENG ; Hsiang-Chu PAI
Asian Nursing Research 2022;16(3):125-133
Purpose:
The purpose of this study was to (1) add relevant contemporary items to develop an assessment instrument for the self-efficacy of clinical nursing teachers, to verify and evaluate the validity and reliability of the instrument, and (2) investigate the self-efficacy of clinical nursing teachers.Method: A cross-sectional study was designed. A total of 205 clinical nursing teachers were recruited in Taiwan. Data were collected using the Clinical Nursing Teacher Self-efficacy Scale. An exploratory factor analysis was performed to examine the scale.
Results:
The degree of self-efficacy of clinical nursing teachers was moderate to high. The 35-item scale showed great psychometric qualities. The Cronbach coefficient of the overall scale was 0.92; and 0.83, 0.91, 0.93, and 0.87 on the four subscales were acceptable. Four factors were extracted by exploratory factor analysis and explained 68.53% of the total variance. Four factors were (i) teachers' professional growth ability, (ii) teaching ability, (iii) clinical nursing competencies, and (iv) personality traits. The highest self-efficacy category evaluated by teachers was personality traits; the second was clinical nursing competencies; after that, teaching ability and teachers’ professional growth ability. The item with the lowest self-efficacy was foreign language ability (English).
Conclusion
Clinical nursing teachers have a moderate to high degree of self-efficacy. This scale with good reliability and validity can be used for the training and evaluation of the self-efficacy of clinical nursing teachers.
2.Investigating Medical Cost and Mortality Among Psychiatric Patients Involuntary Admissions: A Nationwide Propensity Score-Matched Study
Pei-Ying TSENG ; Xin-Yu XIE ; Ching-Chi HSU ; Sarina Hui-Lin CHIEN ; Jen-De CHEN ; Jong-Yi WANG
Psychiatry Investigation 2022;19(7):527-537
Objective:
Involuntary admission to psychiatric inpatient care can protect both patients with severe mental illnesses and individuals around them. This study analyzed annual healthcare costs per person for involuntary psychiatric admission and examined categories of mental disorders and other factors associated with mortality.
Methods:
This retrospective cohort study collected 1 million randomly sampled beneficiaries from the National Health Insurance Database for 2002–2013. It identified and matched 181 patients with involuntary psychiatric admissions (research group) with 724 patients with voluntary psychiatric admissions (control group) through 1:4 propensity-score matching for sex, age, comorbidities, mental disorder category, and index year of diagnosis.
Results:
Mean life expectancy of patients with involuntary psychiatric admissions was 33.13 years less than the general population. Average annual healthcare costs per person for involuntary psychiatric admissions were 3.94 times higher compared with voluntary admissions. The general linear model demonstrated that average annual medical costs per person per compulsory hospitalization were 5.8 times that of voluntary hospitalization. Survival analysis using the Cox proportional hazards model found no significant association between type of psychiatric admission (involuntary or voluntary) and death.
Conclusion
This study revealed no significant difference in mortality between involuntary and voluntary psychiatric admissions, indicating involuntary treatment’s effectiveness.
3.Outpatient varicocelectomy performed under local anesthesia.
Geng-Long HSU ; Pei-Ying LING ; Cheng-Hsing HSIEH ; Chii-Jye WANG ; Cheng-Wen CHEN ; Hsien-Sheng WEN ; Hsiu-Mei HUANG ; E Ferdinand EINHORN ; Guo-Fang TSENG
Asian Journal of Andrology 2005;7(4):439-444
AIMTo report a series of varicocelectomy performed under pure local anesthesia.
METHODSFrom July 1988 to June 2003, a total of 575 patients, aged between 15 and 73 years, underwent high ligation of the internal spermatic vein for treatment of a varicocele testis under a regional block in which a precise injection of 0.8 % lidocaine solution was delivered to involved tissues after exact anatomical references were made. A 100-mm visual analog scale (VAS) was used to assess whether the pain level was acceptable.
RESULTSThe surgeries were bilateral in 52 cases, and unilateral in 523 cases. All were successfully performed on an outpatient basis except in the case of two patients, who were hospitalized because their surgeries required general anesthesia. Overall, 98.6 % (567/575) of men could go back to work by the end of the first post-operative week and only 8 (1.4 %) men reported feeling physical discomfort on the eighth day. The VAS scores varied from 11 mm to 41 mm with an average of (18.5+/-11.3) mm that was regarded as tolerable.
CONCLUSIONThis study has shown varicocelectomy under local anesthesia to be possible, simple, effective, reliable and reproducible, and a safe method with minimal complications. It offers the advantages of more privacy, lower morbidity, with no notable adverse effects resulting from anesthesia, and a more rapid return to regular physical activity with minor complications.
Acetaminophen ; administration & dosage ; Adolescent ; Adult ; Aged ; Analgesics, Non-Narcotic ; administration & dosage ; Anesthesia, Local ; Anesthetics, Local ; administration & dosage ; Follow-Up Studies ; Humans ; Lidocaine ; administration & dosage ; Male ; Middle Aged ; Outpatients ; Pain, Postoperative ; drug therapy ; Postoperative Complications ; Varicocele ; surgery ; Vascular Surgical Procedures ; methods
4.Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program
Ming-Ying LU ; Chung-Feng HUANG ; Chao-Hung HUNG ; Chi‐Ming TAI ; Lein-Ray MO ; Hsing-Tao KUO ; Kuo-Chih TSENG ; Ching-Chu LO ; Ming-Jong BAIR ; Szu-Jen WANG ; Jee-Fu HUANG ; Ming-Lun YEH ; Chun-Ting CHEN ; Ming-Chang TSAI ; Chien-Wei HUANG ; Pei-Lun LEE ; Tzeng-Hue YANG ; Yi-Hsiang HUANG ; Lee-Won CHONG ; Chien-Lin CHEN ; Chi-Chieh YANG ; Sheng‐Shun YANG ; Pin-Nan CHENG ; Tsai-Yuan HSIEH ; Jui-Ting HU ; Wen-Chih WU ; Chien-Yu CHENG ; Guei-Ying CHEN ; Guo-Xiong ZHOU ; Wei-Lun TSAI ; Chien-Neng KAO ; Chih-Lang LIN ; Chia-Chi WANG ; Ta-Ya LIN ; Chih‐Lin LIN ; Wei-Wen SU ; Tzong-Hsi LEE ; Te-Sheng CHANG ; Chun-Jen LIU ; Chia-Yen DAI ; Jia-Horng KAO ; Han-Chieh LIN ; Wan-Long CHUANG ; Cheng-Yuan PENG ; Chun-Wei- TSAI ; Chi-Yi CHEN ; Ming-Lung YU ;
Clinical and Molecular Hepatology 2024;30(1):64-79
Background/Aims:
Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy.
Methods:
We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment.
Results:
The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset.
Conclusions
Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.