Dataset Information

Identifying Factors That Predict Behavioral Intention to Stay under Lockdown during the SARS-CoV-2 Pandemic Using a Structural Equation Model.

ABSTRACT: Lockdown is considered to be a successful strategy for preventing the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To predict the behavioral intention to stay under lockdown (BIKL), components of the theory of planned behavior (TPB) and the behavioral indicators of infection were applied. Sampling was conducted between 11 April and 30 May 2020. The objective of the study was to identify factors predictive of BIKL by means of a structural equation model. A correlational and comparative repeated measures study was conducted with a sample of 315 participants from different cities in Mexico. Model indices were χ2 = 505.1, SD = 228, p < 0.001, χ2/SD = 2.2, CFI = 0.91, RMSEA = 0.06, and SRMR = 0.06; 47% of BIKL was explained by the variables attitude (β = 0.71, p < 0.001), subjective norm (β = 0.14, p = 0.042), and behavioral control (β = 0.24, p < 0.001). Personal and family work conviction and persuasion are favorable for the maintenance of lockdown, including concepts of civic responsibility, a positive attitude, and a family that supports compliance with lockdown. From a governmental point of view, there is a context that promotes control over the situation and exerts a positive impact on the behavioral intention to stay under lockdown.

SUBMITTER: Padilla-Bautista JA

PROVIDER: S-EPMC8910594 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Identifying Factors That Predict Behavioral Intention to Stay under Lockdown during the SARS-CoV-2 Pandemic Using a Structural Equation Model.

Padilla-Bautista Joaquin Alberto JA Galindo-Aldana Gilberto Manuel GM

International journal of environmental research and public health 20220227 5

Lockdown is considered to be a successful strategy for preventing the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To predict the behavioral intention to stay under lockdown (BIKL), components of the theory of planned behavior (TPB) and the behavioral indicators of infection were applied. Sampling was conducted between 11 April and 30 May 2020. The objective of the study was to identify factors predictive of BIKL by means of a structural equation model. Method: A corre ...[more]

PMID: 35270450

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Project description:BackgroundSuicidality is a serious public health concern at a global scale. Suicide itself is considered to be preventable death; worldwide, suicide rates and their trends are under constant scrutiny. As part of the international COMET-G cross-sectional study, we conducted a national level investigation to examine the individual disturbances (such as anxiety, depression, or history of life-threatening attempts) and contextual factors (such as adherence to conspiracy theories or Internet use) associated with suicidality related to the COVID-19 lockdown in a lot of Romanian adults.Participants and methodsOne thousand four hundred and forty-six adults responded to an anonymous on-line questionnaire, with mean age ± standard deviation of 47.03 ± 14.21 years (1,142 females, 292 males, 12 identified themselves as non-binary). Data were analyzed using descriptive statistics and structural equation modeling (SEM).ResultsUnivariate analysis showed strong significant correlation between anxiety and depression scorings among the respondents (Spearman R = 0.776, p < 0.001). Both the suicidality scorings and the Internet use correlated fairly with anxiety and depression, with two-by-two Spearman coefficients between R = 0.334 and R = 0.370 (p < 0.001 for each). SEM analysis substantiated the emotional disturbances, previous life-threatening attempts, and younger age as significant predictors for suicidality. The patterns of reality reading (including religious inquiries, Internet use, and beliefs in conspiracy theories) did not reach the statistical significance as influential factors in the suicidality of these respondents. There was no covariance between the Internet use and belief in conspiracy theories.ConclusionThe study confirmed the suicidality risk initially hypothesized as being associated with the history of life-threatening attempts, increased depression within the younger population, and higher anxiety during the first year of the COVID-19 pandemic and its related lockdown. National strategies for effective interventions at various levels of the healthcare system should be developed.

Project description:Background contextLength-of-hospital stay (LOS) after lumbar spine surgery (LSS) can be affected by many factors. However, few studies have evaluated predictors of LOS, and all have used limited number of variables as predictors.PurposeThe purpose of the study was to identify presurgical, surgical, and postsurgical predictors of LOS after LSS.Study design/settingRetrospective review of consecutive patients who had LSS at the University of Kansas Hospital from October 2008 to April, 2012.Patient sampleFive hundred ninety-three patients underwent LSS consisting of laminotomy, laminectomy, or arthrodesis.Outcome measuresDependent variableLOS. Multiple presurgical, surgical, and postsurgical variables were extracted from the patients' medical records and considered as possible predictors (independent variables) of LOS.MethodsPotential predictors that were significantly correlated with LOS were used as indicators to construct three latent factors presurgical, surgical, and postsurgical, which were in turn used to predict LOS in a structural equation model.ResultsThe average LOS was 4.01±2.73 days. The presurgical factor was indicated by age (61.97±14.49 years), previous level of function (60.5% were totally independent), previous hemoglobin level (13.70±1.36 mg/dL), and use of assistive devices (60% were assistive device users). The surgical factor was indicated by severity of illness (50.2% had minor disease severity), presence of complications (1.9%), and stay in an intensive care unit (4.0%). The postsurgical factor was indicated by postsurgical walking distance (166.43±175.75 ft), level of assistance during walking (5.18±0.81 out of 7 points), balance scores (6.18±1.82 out of 10 points), and bed mobility and transfer dependency scores (9.81±1.99 out of 14 points). These three latent factors explained 47% of variation in LOS.ConclusionsPostsurgical factors predicted the highest variation in LOS in comparison with presurgical and surgical factors and should be taken into consideration for discharge planning. Postsurgical factors are related to the patient's function, modifiable with rehabilitation, and can be improved to shorten LOS. Inclusion of more reliable and standardized presurgical variables could improve the predictability of the model.

Dataset Information

Identifying Factors That Predict Behavioral Intention to Stay under Lockdown during the SARS-CoV-2 Pandemic Using a Structural Equation Model.

Publications

Identifying Factors That Predict Behavioral Intention to Stay under Lockdown during the SARS-CoV-2 Pandemic Using a Structural Equation Model.

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