Do Meta-Analyses of Intervention/Prevention Programs in the Field of Criminology Meet the Tests of Transparency and Reproducibility?

Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices

Cardiovascular disease remains a leading cause of morbidity and mortality in the United States (US). Although high-quality data are accessible in the US for cardiovascular research, digital literacy (DL) has not been explored as a potential factor influencing cardiovascular mortality, although the Social Vulnerability Index (SVI) has been used previously as a variable in predictive modeling. Utilizing a large language model, ChatGPT4, we investigated the variability in CVD-specific mortality that could be explained by DL and SVI using regression modeling. We fitted two models to calculate the crude and adjusted CVD mortality rates. Mortality data using ICD-10 codes were retrieved from CDC WONDER, and the geographic level data was retrieved from the US Department of Agriculture. Both datasets were merged using the Federal Information Processing Standards code. The initial exploration involved data from 1999 through 2020 (n = 65,791; 99.98% complete for all US Counties) for crude cardiovascular mortality (CCM). Age-adjusted cardiovascular mortality (ACM) had data for 2020 (n = 3118 rows; 99% complete for all US Counties), with the inclusion of SVI and DL in the model (a composite of literacy and internet access). By leveraging on the advanced capabilities of ChatGPT4 and linear regression, we successfully highlighted the importance of incorporating the SVI and DL in predicting adjusted cardiovascular mortality. Our findings imply that just incorporating internet availability in the regression model may not be sufficient without incorporating significant variables, such as DL and SVI, to predict ACM. Further, our approach could enable future researchers to consider DL and SVI as key variables to study other health outcomes of public-health importance, which could inform future clinical practices and policies.

Advice for improving the reproducibility of data extraction in meta-analysis

Extracting data from studies is the norm in meta-analyses, enabling researchers to generate effect sizes when raw data are otherwise not available. While there has been a general push for increased reproducibility in meta-analysis, the transparency and reproducibility of the data extraction phase is still lagging behind. Unfortunately, there is little guidance of how to make this process more transparent and shareable. To address this, we provide several steps to help increase the reproducibility of data extraction in meta-analysis. We also provide suggestions of R software that can further help with reproducible data policies: the shinyDigitise and juicr packages. Adopting the guiding principles listed here and using the appropriate software will provide a more transparent form of data extraction in meta-analyses.

Are risk-need-responsivity principles golden? A meta-analysis of randomized controlled trials of community correction programs

Objectives

Using meta-analysis to determine the effect size of the recidivism rate of participants in community correction programs that are conducted entirely in community settings.

Methods

Following the Preferred Reported Items for Systematic Reviews and Meta-analyses (PRISMA), 25 qualified studies contributed 35 independent effect sizes.

Results

Full participation in a program significantly reduced the recidivism rate. Participant age was a significant moderator of heterogeneity. Those aged over 18 have lower recidivism rates. Interventions that fully follow the Risk-Need-Responsivity (RNR) design principles achieved similar results to those that did not. Recidivism rates increase more than 12 months after the program ends.

Conclusions

The effectiveness of community correctional programs varies depending on the participant's age. The RNR principles are not golden. The above factors should be carefully considered when conducting intervention design in the future. Results should be interpreted with caution due to the literature's high heterogeneity and low quality.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11292-022-09550-w.