Chinese Americans’ Family History of Colorectal Cancer Communication With Primary Care Physicians

2021 ◽  
pp. 109019812110097
Author(s):  
Yu-Lyu Yeh ◽  
Ming Li ◽  
Oi-Man Kwok ◽  
Ping Ma ◽  
Lei-Shih Chen
Keyword(s):  
Colorectal Cancer ◽  
Primary Care ◽  
Structural Equation Modeling ◽  
Family History ◽  
Root Mean Square ◽  
Primary Care Physicians ◽  
Structural Equation ◽  
Chinese Americans ◽  
Equation Modeling ◽  
Mean Square

Background Colorectal cancer (CRC) is the third most common cancer for Chinese Americans. Family history (FH) plays an important role in clinical practice for CRC prevention. Nevertheless, Chinese Americans’ FH of CRC communication with primary care physicians (PCPs) are still unknown. Aims This study examined Chinese Americans’ behavior and the underlying psychological factors for FH of CRC communication with PCPs. Method A total number of 742 Chinese Americans completed a survey developed based on the health belief model, the theory of planned behavior, and the social cognitive theory. Data were analyzed using structural equation modeling. Results Majority of the Chinese American participants (75.3%) had never discussed FH of CRC with their PCPs. Lack of inquiries from the PCPs was the main barrier. Structural equation modeling results suggested a good model fit between our theoretical model and the survey data (comparative fit index [CFI] = .946, root mean square error of approximation [RMSEA] = .070, and standardized root mean square residual [SRMR] = .020). Participants’ FH of CRC communication with PCPs was positively associated with their intention (β = .30, p < .001), which was positively correlated to attitudes (β = .29, p < .001) and self-efficacy in discussing FH of CRC with PCPs (β = .57, p < .001). Their attitudes were positively associated with perceived susceptibility to CRC (β = .08, p < .05) and the perceived benefits of communicating FH of CRC (β = .52, p < .001). Conclusions Given that most Chinese Americans in this study did not communicate their FH of CRC with their PCPs, it is important to promote such behavior among Chinese Americans. Our structural equation modeling findings can guide future interventions and education for this underserved racial/ethnic minority group.

scholarly journals Population-level Preferences for Primary Care Physicians' Characteristics in Japan: A Structural Equation Modeling

2010 ◽  
Vol 49 (2) ◽  
pp. 125-130 ◽  
Author(s):  
Osamu Takahashi ◽  
Sachiko Ohde ◽  
Joshua L. Jacobs ◽  
Yasuharu Tokuda ◽  
Haruo Yanai ◽  
...  
Keyword(s):  
Primary Care ◽  
Structural Equation Modeling ◽  
Primary Care Physicians ◽  
Structural Equation ◽  
Population Level ◽  
Equation Modeling

The Adult-Oriented Sport Coaching Survey: An Instrument Designed to Assess Coaching Behaviors Tailored to Adult Athletes

2020 ◽  
Vol 42 (5) ◽  
pp. 368-385
Author(s):  
Scott Rathwell ◽  
Bradley W. Young ◽  
Bettina Callary ◽  
Derrik Motz ◽  
Matt D. Hoffmann ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Root Mean Square ◽  
Structural Equation ◽  
Equation Modeling ◽  
Mean Square ◽  
Root Mean Square Residual ◽  
Confirmatory Factor ◽  
Masters Athletes ◽  
Fit Index

Adult sportspersons (Masters athletes, aged 35 years and older) have unique coaching preferences. No existing resources provide coaches with feedback on their craft with Masters athletes. Three studies evaluated an Adult-Oriented Coaching Survey. Study 1 vetted the face validity of 50 survey items with 12 Masters coaches. Results supported the validity of 48 items. In Study 2, 383 Masters coaches completed the survey of 50 items. Confirmatory factor analysis and exploratory structural equation modeling indicated issues with model fit. Post hoc modifications improved fit, resulting in a 22-item, five-factor model. In Study 3, 467 Masters athletes responded to these 22 items reflecting perceptions of their coaches. Confirmatory factor analysis (comparative fit index = .951, standardized root mean square residual = .036, and root mean square error of approximation = .049) and exploratory structural equation modeling (comparative fit index = .977, standardized root mean square residual = .019, and root mean square error of approximation = .041) confirmed the model. The resultant Adult-Oriented Sport Coaching Survey provides a reliable and factorially valid instrument for measuring adult-oriented coaching practices.

The Effect of Latent and Error Non-Normality on Measures of Fit in Structural Equation Modeling

2021 ◽  
pp. 001316442110462
Author(s):  
Lisa J. Jobst ◽  
Max Auerswald ◽  
Morten Moshagen
Keyword(s):  
Structural Equation Modeling ◽  
Root Mean Square ◽  
Latent Variables ◽  
Structural Equation ◽  
Generalized Least Squares ◽  
Estimation Methods ◽  
Equation Modeling ◽  
Analytic Structure ◽  
Mean Square ◽  
Test Statistic

Prior studies investigating the effects of non-normality in structural equation modeling typically induced non-normality in the indicator variables. This procedure neglects the factor analytic structure of the data, which is defined as the sum of latent variables and errors, so it is unclear whether previous results hold if the source of non-normality is considered. We conducted a Monte Carlo simulation manipulating the underlying multivariate distribution to assess the effect of the source of non-normality (latent, error, and marginal conditions with either multivariate normal or non-normal marginal distributions) on different measures of fit (empirical rejection rates for the likelihood-ratio model test statistic, the root mean square error of approximation, the standardized root mean square residual, and the comparative fit index). We considered different estimation methods (maximum likelihood, generalized least squares, and (un)modified asymptotically distribution-free), sample sizes, and the extent of non-normality in correctly specified and misspecified models to investigate their performance. The results show that all measures of fit were affected by the source of non-normality but with varying patterns for the analyzed estimation methods.

scholarly journals Erfassung der Emotionsregulation bei Jugendlichen anhand des „Affective Style Questionnaire – Youth (ASQ-Y)“

Diagnostica ◽  
2019 ◽  
Vol 65 (1) ◽  
pp. 49-59
Author(s):  
Johannes Graser ◽  
Christiane Heimlich ◽  
Augustin Kelava ◽  
Stefan G. Hofmann ◽  
Ulrich Stangier ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Root Mean Square Error ◽  
Root Mean Square ◽  
Structural Equation ◽  
Equation Modeling ◽  
Mean Square ◽  
Affective Style ◽  
Root Mean Square Residual ◽  
Fit Index ◽  
Tucker Lewis Index

Zusammenfassung. Zur Erfassung der 3 Emotionsregulationsstrategien Unterdrücken, Anpassen / Neubewerten und Akzeptieren wurde der Affective Style Questionnaire für Jugendliche (ASQ-Y) adaptiert und an einer entsprechenden Stichprobe (N = 1 092) validiert. Die Dimensionalität des englischen Originalfragebogens und der deutschen Version für Erwachsene konnte auch für Jugendliche bestätigt werden. Während der Analyse kam das ESEM-Verfahren (Exploratory Structural Equation Modeling) zum Einsatz, die Kennwerte bewegten sich im akzeptablen bis sehr guten Bereich. Der Comparative Fit Index (CFI) erreichte einen akzeptablen Wert von .938, ebenso der Tucker–Lewis Index (TLI) mit einem Wert von .911. Der Root Mean Square Error of Approximation (RMSEA) lag bei einem sehr guten Wert von .050, das Standardized Root Mean Square Residual (SRMR) erreichte einen guten Wert von .030. Die internen Konsistenzen der 3 Skalen (Unterdrücken: α = .77; Anpassen / Neubewerten: α = .76; Akzeptieren: α = .76) erreichten (vergleichbar mit dem englischen Original und der deutschen Erwachsenenstichprobe) zufriedenstellende Werte. Die Subskalen zeigten hypothesenkonforme diskriminante und konvergente Zusammenhänge mit etablierten Verfahren des Forschungsbereichs Emotionsregulation, was für die Konstruktvalidität spricht. Insgesamt ist der ASQ-Y als Messinstrument zur Erfassung von verschiedenen Emotionsregulationsstrategien bei Jugendlichen geeignet und ökonomisch in seiner Anwendung. Der ASQ-Y kann in der Allgemeinbevölkerung und in der Prävention eingesetzt werden. Nach entsprechender Validierung ist der Einsatz auch im klinischen Setting möglich.

scholarly journals Identifying Primary Care Pathways from Quality of Care to Outcomes and Satisfaction Using Structural Equation Modeling

2017 ◽  
Vol 53 (1) ◽  
pp. 430-449 ◽  
Author(s):  
Ignacio Ricci-Cabello ◽  
Sarah Stevens ◽  
Andrew R. H. Dalton ◽  
Robert I. Griffiths ◽  
John L. Campbell ◽  
...  
Keyword(s):  
Primary Care ◽  
Quality Of Care ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Care Pathways

scholarly journals Structural equation modeling for identification of patient safety antecedents in primary care

2020 ◽  
Author(s):  
Katarzyna Kosiek ◽  
Iwona Staniec ◽  
Maciej Godycki-Cwirko ◽  
Adam Depta ◽  
Anna Kowalczyk
Keyword(s):  
Primary Care ◽  
Patient Safety ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Safety Management ◽  
Improve Patient Safety ◽  
Equation Modeling ◽  
Number Of Factors ◽  
Safety Interventions ◽  
Improve Patient

Abstract Background:. Patient safety is defined as an activity that minimizes and removes possible errors and injuries to patients. A number of factors have been found to influence patient safety management, including the facilities available in the practice, communication and collaboration, education regarding patient safety and generic conditions. This study tested a theoretical model of patient safety interventions based on safety antecedents. Methods: Medical professionals were surveyed using a questionnaire developed by Gaal et al. The results were analyzed with SPSS 20 and AMOS. A hypothetical model of direct and indirect effects on patient safety in a primary care environment was created and analyzed using structural equation modeling (SEM). Results: SEM proved to be an effective tool to analyse safety in primary care. The facilities in the practice appear to have no significant influence on patient safety management in the case of female respondents, those below mean age, those who are not GPs (general practitioner) and respondents not working in counselling centres.Conclusions: The integrated safety model described in the study can improve patient safety management.

scholarly journals Knowledge, Attitudes and Intentions to Prescribe Antibiotics: A Structural Equation Modeling Study of Primary Care Institutions in Hubei, China

2019 ◽  
Vol 16 (13) ◽  
pp. 2385 ◽  
Author(s):  
Chenxi Liu ◽  
Chaojie Liu ◽  
Dan Wang ◽  
Xinping Zhang
Keyword(s):  
Primary Care ◽  
Antibiotic Resistance ◽  
Primary Care Physicians ◽  
Structural Equation ◽  
Antibiotic Use ◽  
Equation Modeling ◽  
Antibiotic Prescribing ◽  
Prescribing Practices ◽  
Motivation To Change ◽  
Attitudes And Practices

The aim of this paper is to measure the knowledge and attitudes of primary care physicians toward antibiotic prescriptions and their impacts on antibiotic prescribing. A questionnaire survey was conducted on 625 physicians from 67 primary care facilities in Hubei, China. Structural equation modelling (SEM) was applied to test the theoretical framework derived from the Knowledge, Attitudes, and Practices (KAP) theory. Physicians’ knowledge, five sub-types of attitudes, and three sub-types of behavioral intentions towards antibiotic use were measured. Physicians had limited knowledge about antibiotic prescriptions (average 54.55% correct answers to 11 questions). Although they were generally concerned about antibiotic resistance (mean = 1.28, SD = 0.43), and were reluctant to be submissive to pressures from consumer demands for antibiotics (mean = 1.29, SD = 0.65) and the requirements of defensive practice (mean = 1.11, SD = 0.63), there was a lack of motivation to change prescribing practices (mean = −0.29, SD = 0.70) and strong agreement that other stakeholders should take the responsibility (mean = −1.15, SD = 0.45). The SEM results showed that poor knowledge, unawareness of antibiotic resistance, and limited motivation to change contributed to physicians’ high antibiotics prescriptions (p < 0.001). To curb antibiotic over-prescriptions, improving knowledge itself is not enough. The lack of motivation of physicians to change needs to be addressed through a systematic approach.

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