We imputed the mean changes (before, after) and the pooled standard deviation of the study outcome values in the meta-analysis. All analyses related to meta-analysis were performed using a model with random effects. Estimated values for pooled effect sizes in all learning outcomes were shown with Standardized Mean Differences (SMD); this method was chosen due to the different parameter scales in the selected studies. Heterogeneity was assessed with I2, τ2 tests, while publication bias was evaluated by Egger’s, Begg’s test. All analyses were conducted in Stata v16 and EndNote X9 was applied for resource management. Hence, Mean Gain (MG) and SD Pooled from pre and post-intervention were inputted into Stata v16.
Stata v 16
Manufactured by StataCorp
Sourced in United States, Austria
Stata V.16 is a comprehensive statistical software package developed by StataCorp. It provides a wide range of data management, statistical analysis, and visualization tools for researchers and professionals across various fields. Stata V.16 offers a user-friendly interface and a powerful programming language to facilitate data analysis and reporting.
Automatically generated - may contain errors
Lab products found in correlation
Sas v9, by SAS Institute (10 mentions)
Spss v25, by IBM (7 mentions)
Stata statistical software release 16, by StataCorp (5 mentions)
Prism v9, by GraphPad (4 mentions)
Prism v8, by GraphPad (4 mentions)
Sas v9, by StataCorp (3 mentions)
Prism 8, by GraphPad (2 mentions)
Ms excel, by StataCorp (2 mentions)
Spss v22, by IBM (2 mentions)
Sas version 9, by SAS Institute (2 mentions)
Prism v 5, by StataCorp (2 mentions)
Prism v 8, by StataCorp (2 mentions)
Stata statistical software, by StataCorp (2 mentions)
Epi info v 7, by StataCorp (2 mentions)
Prism 9, by GraphPad (2 mentions)
Sas software, by SAS Institute (2 mentions)
Spss v26, by IBM (2 mentions)
Spss software for windows v 20, by IBM (1 mentions)
Nvivo v 11, by Lumivero (1 mentions)
Sas 9, by StataCorp (1 mentions)
752 protocols using stata v 16
1
Meta-Analysis of Learning Outcomes
2
Comprehensive Meta-Analysis of Research Protocols
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SMDs from continuous outcome variables and ORs or prevalence ratios for dichotomous outcome variables will be synthesised separately. Effect sizes will be pooled statistically using inverse variance weighted random effects meta-analysis, using the metan command in Stata V.16. Pooled effects will be expressed in metric that is relevant, for example, a percentage change in odds, or a mean difference measured in natural units of outcome.
The synthesis will further be in form of summary of findings tables, simple graphs and forest plots as applicable using a Stata V.16. This will follow the format of the Cochrane consumers and communication review group.31 We shall describe the included articles, group articles according to study design and type of intervention, organise and tabulate results to identify patterns and transform the results into a common descriptive format. These will be in form of outcome data tables, simple graphs and forest plots as applicable. These will feed into the summary of findings tables that inform the syntheses for sharing. We shall thus use both narrative and quantitative synthesis.
The synthesis will further be in form of summary of findings tables, simple graphs and forest plots as applicable using a Stata V.16. This will follow the format of the Cochrane consumers and communication review group.31 We shall describe the included articles, group articles according to study design and type of intervention, organise and tabulate results to identify patterns and transform the results into a common descriptive format. These will be in form of outcome data tables, simple graphs and forest plots as applicable. These will feed into the summary of findings tables that inform the syntheses for sharing. We shall thus use both narrative and quantitative synthesis.
3
Assessing Childhood Maltreatment's Impact
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All analyses were performed using Stata v16 (StataCorp LLC., College Station, TX, USA). Univariate comparisons between groups were conducted using the chi-square test (or Fisher’s exact test when the assumption of frequencies for the chi-square test was not met) for qualitative variables and a one-way ANOVA for quantitative variables.
Linear regression models with adjustments for age and gender were used to assess the effect of childhood maltreatment on scales assessing emotion reactivity, cognitive emotion regulation strategies, and attachment style in each clinical group separately. For analyses of the whole population, an additional adjustment for diagnostic group as the fixed effect was made. Statistical significance was accepted for p-values < 0.05.
A mediation analysis was used to assess the mediating effect of attachment style on the relationship between CTQ total score and emotion-related scales. Mediation analyses were only done in the whole population with adjustments for age, gender, and diagnostic group considering variables that showed a significant association with CTQ total score at a level of p < 0.001. Only total scores were considered. The methods described by Hicks and Tingley [85 (link)] using the “medeff” with 1000 simulations and 1000 bootstraps implemented in Stata v16 (StataCorp LLC., College Station, TX, USA) were applied.
Linear regression models with adjustments for age and gender were used to assess the effect of childhood maltreatment on scales assessing emotion reactivity, cognitive emotion regulation strategies, and attachment style in each clinical group separately. For analyses of the whole population, an additional adjustment for diagnostic group as the fixed effect was made. Statistical significance was accepted for p-values < 0.05.
A mediation analysis was used to assess the mediating effect of attachment style on the relationship between CTQ total score and emotion-related scales. Mediation analyses were only done in the whole population with adjustments for age, gender, and diagnostic group considering variables that showed a significant association with CTQ total score at a level of p < 0.001. Only total scores were considered. The methods described by Hicks and Tingley [85 (link)] using the “medeff” with 1000 simulations and 1000 bootstraps implemented in Stata v16 (StataCorp LLC., College Station, TX, USA) were applied.
4
Newborn Screening Analytes and Type 1 Diabetes
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Categorical data were compared using chi-square testing. It was decided a priori that the primary outcome would be free carnitine concentrations (CAR) as this had previously been demonstrated [23 (link)]. A secondary analysis of 22 analytes to explore other associations would then be performed (See Supplementary Table S1 ). Multiple imputation was used to account for missing gestational age for 12 cases (8%). Data augmentation algorithm in Stata (V16) MI procedure was used to generate 30 imputed data sets [24 (link)]. Parameter estimation using conditional logistic regression on each filled-in data set was performed to evaluate the relationship between type 1 diabetes development and each analyte, adjusting for gestational age. Rubin’s formulae were used to combine the parameter estimates and standard errors into a single set of results [25 ]. Hommel’s step-up technique was used for post-hoc analysis again using Stata (V16).
Newborn screening analytes are not normally distributed and reported as medians. Analytes added, as the program progressed, to improve sensitivity and predictive values were included in the analysis. Alanine is no longer part of routine NBS in Australia due to lack of clinical utility.
Newborn screening analytes are not normally distributed and reported as medians. Analytes added, as the program progressed, to improve sensitivity and predictive values were included in the analysis. Alanine is no longer part of routine NBS in Australia due to lack of clinical utility.
5
Systematic Analysis of Respiratory Outcomes
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The extracted data, including FVC, FEV1 and DLCO, were analysed using Revman V.5.4 software (Cochrane Collaboration). Single-group results (eg, non-progression rate of HRCT and fatality rate) were pooled and analysed by using STATA V.16.0. For continuous outcomes, pooled outcomes were presented as a mean difference (MD) and 95% CI for analysis, while for single-group rates, effect size and 95% CI were used for analysis. The I2 value was used to evaluate heterogeneity. Generally, we used the fixed-effects model to analyse substantial homogeneous trials (I2≤50%, p>0.1). When statistical heterogeneity existed (I2>50%, p<0.1), we used a random-effects model followed by sensitivity analyses and subgroup analyses, which were carried out by gradually removing studies and performed according to types of drugs. STATA V.16.0 software and Egger’s test were used to evaluate publication bias for studies involving FVC and DLCO.
6
Systematic Analysis of Respiratory Outcomes
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The extracted data, including FVC, FEV1 and DLCO, were analysed using Revman V.5.4 software (Cochrane Collaboration). Single-group results (eg, non-progression rate of HRCT and fatality rate) were pooled and analysed by using STATA V.16.0. For continuous outcomes, pooled outcomes were presented as a mean difference (MD) and 95% CI for analysis, while for single-group rates, effect size and 95% CI were used for analysis. The I2 value was used to evaluate heterogeneity. Generally, we used the fixed-effects model to analyse substantial homogeneous trials (I2≤50%, p>0.1). When statistical heterogeneity existed (I2>50%, p<0.1), we used a random-effects model followed by sensitivity analyses and subgroup analyses, which were carried out by gradually removing studies and performed according to types of drugs. STATA V.16.0 software and Egger’s test were used to evaluate publication bias for studies involving FVC and DLCO.
7
Healthcare Workers' Knowledge on Nosocomial Infection Prevention
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The data was extracted from Google Forms to Excel Sheet for cleaning and then exported into STATA V.16.0 (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC.) analysis. To ensure the quality of the data extracted, double entry was done to address discrepancies which may have occurred during data extraction. The data was extensively cleaned again in STATA V. 16.0 (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC.) before analysis was carried out. Descriptive statistics were performed to interpret the socio-demographic features including age, sex, level of education, work experience, and in-service training or workshop on nosocomial infection prevention. However, inferential statistics were done to test the association between socio-demographic factors and healthcare workers’ knowledge in preventive measures of nosocomial infections. Frequencies and percentages related to the study findings were presented using tables and graphs.
8
Burnout Factors Among Healthcare Workers
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Data were entered through the REDCap platform, hosted by the University of Witwatersrand.11 (link),12 (link) Data were exported directly from REDCap to STATA V.16.1 for analysis. Categorical variables collected were coded numerically and continuous variables were cleaned and standardised in terms of units measured in STATA V.16.1 in preparation for analysis.
Descriptive statistics were used to summarise the characteristics of the data set. A bivariate analysis (an analysis conducted to examine the relationship between two variables) was used to examine the relationship between each individual and workplace variable with each of the three burnout subscales of Emotional Exhaustion, Depersonalisation and Personal Accomplishment. The burnout subscales were the dependent variable. Statistically significant associations were examined using the crude estimate of 0.25. Multiple linear regression was used to examine any factors associated with burnout outcomes. A multiple linear regression model was built to account for confounding, using stepwise variable selection. A significance of 5% was used for statistical significance testing.
Descriptive statistics were used to summarise the characteristics of the data set. A bivariate analysis (an analysis conducted to examine the relationship between two variables) was used to examine the relationship between each individual and workplace variable with each of the three burnout subscales of Emotional Exhaustion, Depersonalisation and Personal Accomplishment. The burnout subscales were the dependent variable. Statistically significant associations were examined using the crude estimate of 0.25. Multiple linear regression was used to examine any factors associated with burnout outcomes. A multiple linear regression model was built to account for confounding, using stepwise variable selection. A significance of 5% was used for statistical significance testing.
9
Weighted Cluster Sampling Methodology
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95% CI was used to report uncertainty of our CSRs and inequality estimates. Calculation of the 95% CIs is accounted for the two-stage cluster sampling design by applying survey weights appropriately. This also made the results representative of the national and regional corresponding populations.22 All the analyses were conducted using Stata V.16 software (StataCorp. 2019. V.16. College Station, Texas, USA: StataCorp LLC) and all figures were plotted using ggplot2 package in R programming software (R Core Team (2019). R Foundation for Statistical Computing, Vienna, Austria).
10
Cervical Cancer Knowledge and Gender Equity
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Descriptive statistics were performed to summarize demographic variables and all information about cervical cancer knowledge and awareness, gender beliefs, and household decision-making. A knowledge score was calculated based on responses to true/false statements about cervical cancer disease and services (maximum = 16 points, indicating higher knowledge). A GEM score was calculated from the level of agreement with 8 statements about gender norms (maximum = 16 points, indicating more support for gender equity). Sub-domain scores were also calculated within GEM based on questions about sexual relationships and violence. Bivariate analyses compared GEM and knowledge scores based on reported screening history of the respondent’s partner, using appropriate statistical tests (Pearson’s chi-squared test for categorical variables, two independent samples t-test for continuous variables) and a significance level of p < 0.05. Multivariate logistic regression assessed the association between GEM and knowledge scores with partner screening status. Covariate selection was determined through the use of a Directed Acyclic Graph (DAG) for each model (Additional file 3 ). All analyses were conducted using Stata v16 software (StataCorp 2019).
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