Short Scale for Measuring General Self-efficacy Beliefs (ASKU)

Beierlein, Constanze; Kemper, Christoph; Kovaleva, Anastassiya; Rammstedt, Beatrice

doi:10.12758/mda.2013.014

Cited by 43 publications

(13 citation statements)

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“…We used a three item short scale to measure individual general self-efficacy beliefs [Allgemeine Selbstwirksamkeit Kurzskala (AKSU); Beierlein et al, 2017]. Items were rated on a five-point Likert scale (1 = strongly disagree, 5 = strongly agree).…”

Section: Relationship Scales Questionnairementioning

confidence: 99%

Assessing Patient Preferences: Examination of the German Cooper-Norcross Inventory of Preferences

2022

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Despite the positive effects of including patients’ preferences into therapy on psychotherapy outcomes, there are still few thoroughly validated assessment tools at hand. We translated the 18-item Cooper-Norcross Inventory of Preferences (C-NIP) into German and aimed at replicating its factor structure. Further, we investigated the reliability of the questionnaire and its convergence with trait measures. A heterogeneous sample of N = 969 participants took part in our online survey. Performing ESEM models, we found acceptable model fit for a four-factor structure similar to the original factor structure. Furthermore, we propose an alternative model following the adjustment of single items. The German C-NIP showed acceptable to good reliability, as well as small correlations with Big-Five personality traits, trait and attachment anxiety, locus of control, and temporal focus. However, we recommend further replication of the factor structure and further validation of the C-NIP.

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Section: Relationship Scales Questionnairementioning

confidence: 99%

Assessing Patient Preferences: Examination of the German Cooper-Norcross Inventory of Preferences

2022

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“…Self-efficacy was assessed using the General Self-Efficacy Short Scale with 3 items on a 5-point Likert scale ranging from “fully disagree” to “fully agree” [ 41 ]. The internal consistency of the scale was good (Cronbach α=.83).…”

Section: Methodsmentioning

confidence: 99%

Comparing the Acceptance of Mobile Hypertension Apps for Disease Management Among Patients Versus Clinical Use Among Physicians: Cross-sectional Survey

Breil¹,

Salewski²,

Apolinário-Hagen³

2022

JMIR Cardio

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Background High blood pressure or hypertension is a vastly prevalent chronic condition among adults that can, if not appropriately treated, contribute to several life-threatening secondary diseases and events, such as stroke. In addition to first-line medication, self-management in daily life is crucial for tertiary prevention and can be supported by mobile health apps, including medication reminders. However, the prescription of medical apps is a relatively novel approach. There is limited information regarding the determinants of acceptance of such mobile health (mHealth) apps among patients as potential users and physicians as impending prescribers in direct comparison. Objective The present study aims to investigate the determinants of the acceptance of health apps (in terms of intention to use) among patients for personal use and physicians for clinical use in German-speaking countries. Moreover, we assessed patients’ preferences regarding different delivery modes for self-care service (face-to-face services, apps, etc). Methods Based on an extended model of the unified theory of acceptance and use of technology (UTAUT2), we performed a web-based cross-sectional survey to explore the acceptance of mHealth apps for self-management of hypertension among patients and physicians in Germany. In addition to UTAUT2 variables, we measured self-reported self-efficacy, eHealth literacy, previous experiences with health apps, perceived threat to privacy, and protection motivation as additional determinants of mHealth acceptance. Data from 163 patients and 46 physicians were analyzed using hierarchical regression and mediation analyses. Results As expected, a significant influence of the unified theory of acceptance and use of technology (UTAUT) predictors on intentions to use hypertension apps was confirmed, especially for performance expectancy. Intention to use was moderate in patients (mean 3.5; SD 1.1; range 1-5) and physicians (mean 3.4, SD 0.9), and did not differ between both groups. Among patients, a higher degree of self-reported self-efficacy and protection motivation contributed to an increased explained variance in acceptance with R2=0.09, whereas eHealth literacy was identified as exerting a positive influence on physicians (increased R2=0.10). Furthermore, our findings indicated mediating effects of performance expectancy on the acceptance among patients but not among physicians. Conclusions In summary, this study has identified performance expectancy as the most important determinant of the acceptance of mHealth apps for self-management of hypertension among patients and physicians. Concerning patients, we also identified mediating effects of performance expectancy on the relationships between effort expectancy and social influence and the acceptance of apps. Self-efficacy and protection motivation also contributed to an increase in the explained variance in app acceptance among patients, whereas eHealth literacy was a predictor in physicians. Our findings on additional determinants of the acceptance of health apps may help tailor educational material and self-management interventions to the needs and preferences of prospective users of hypertension apps in future research.

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“…A total of 47 potential predictors allotted to 82 variables were included in the analyses to identify the predictors of RTW 78 weeks after discharge from rehabilitation ( Table 2 ). The following standardized questionnaires were used to record individual potential predictors: limitations and restrictions in activities and participation using the WHO Disability Assessment Schedule (WHODAS) 2.0 ( 19 ), emotional functions using the Patient Health Questionnaire (PHQ-4) ( 20 ), addictive behavior—alcohol (mis)use using the Alcohol Use Disorders Identification Test (AUDIT-C) ( 21 ), self-efficacy using the General Self-Efficacy Short Scale (ASKU) ( 22 ), resilience using the Resilience Scale (RS-13) ( 23 ), personality traits using the Big Five Inventory (BFI) ( 24 ), and current state of health using the EuroQol five-dimension (EQ-5D-5L) visual analogue scale (EQ-VAS) ( 25 ).…”

Section: Methodsmentioning

confidence: 99%

ICF-based prediction of return to work after trauma rehabilitation: Results of the icfPROreha study in patients with severe musculoskeletal injuries

Kus

Oberhauser

Simmel

et al. 2022

Front. Rehabilit. Sci.

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BackgroundPhysical aspects such as the type and severity of an injury are not the only factors contributing to whether or not a person can return to work (RTW) after a serious injury. A more comprehensive, biopsychosocial approach is needed to understand the complexity of RTW fully. The study aims to identify predictors of RTW 78 weeks after discharge from initial inpatient trauma rehabilitation in patients with severe musculoskeletal injuries using a biopsychosocial perspective.MethodsThis is a prospective multicenter longitudinal study with a follow-up of up to 78 weeks after discharge from trauma rehabilitation. Data on potential predictors were collected at admission to rehabilitation using a comprehensive assessment tool. The status of RTW (yes vs. no) was assessed 78 weeks after discharge from rehabilitation. The data were randomly divided into a training and a validation data set in a ratio of 9:1. On the training data, we performed bivariate and multiple logistic regression analyses on the association of RTW and potential predictors. The final logit model was selected via stepwise variable selection based on the Akaike information criterion. The final model was validated for the training and the validation data.ResultsData from 761 patients (n = 561 male, 73.7%; mean age: 47.5 years, SD 12.3), primarily suffering from severe injuries to large joints and complex fractures of the large tubular bones, could be considered for analyses. At 78 weeks after discharge, 618 patients (81.2%) had returned to work. Eleven predictors remained in the final logit model: general health, current state of health, sensation of pain, limitations and restrictions in activities and participation (disability), professional sector, ongoing legal disputes, financial concerns (assets), personality traits, life satisfaction preaccident, attitude to life, and demand for pension claim. A predicted probability for RTW based on the multiple logistic regression model of 76.3% was revealed as the optimal cut-off score based on the ROC curve.ConclusionA holistic biopsychosocial approach is needed to address RTW and strengthen person-centered treatment and rehabilitation. Patients at risk for no RTW in the long term can already be identified at the onset of rehabilitation.

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Short Scale for Measuring General Self-efficacy Beliefs (ASKU)

Cited by 43 publications

References 0 publications

Assessing Patient Preferences: Examination of the German Cooper-Norcross Inventory of Preferences

Assessing Patient Preferences: Examination of the German Cooper-Norcross Inventory of Preferences

Comparing the Acceptance of Mobile Hypertension Apps for Disease Management Among Patients Versus Clinical Use Among Physicians: Cross-sectional Survey

ICF-based prediction of return to work after trauma rehabilitation: Results of the icfPROreha study in patients with severe musculoskeletal injuries

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