Paper 61: Association between attitudes of stigma toward mental illness and attitudes toward adoption of evidence-based practice within health care providers in Bahrain.

Author

Lee Jones - Senior Biostatistician - Statistical Review

Published

March 7, 2026

References

Al Saif F, Al Shakhoori H, Nooh S, Jahrami H (2019) Association between attitudes of stigma toward mental illness and attitudes toward adoption of evidence-based practice within health care providers in Bahrain. PLoS ONE 14(12): e0225738. https://doi.org/10.1371/journal.pone.0225738

Al Saif, Feras; Al Shakhoori, Hussain; Nooh, Suad; Jahrami, Haitham (2020). Data from: Association between attitudes of stigma toward mental illness and attitudes toward adoption of evidence-based practice within health care providers in Bahrain [Dataset]. Dryad. https://doi.org/10.5061/dryad.4b6v2f0

Disclosure

This reproducibility project was conducted to the best of our ability, with careful attention to statistical methods and assumptions. The research team comprises four senior biostatisticians (three of whom are accredited), with 20 to 30 years of experience in statistical modelling and analysis of healthcare data. While statistical assumptions play a crucial role in analysis, their evaluation is inherently subjective, and contextual knowledge can influence judgements about the importance of assumption violations. Differences in interpretation may arise among statisticians and researchers, leading to reasonable disagreements about methodological choices.

Our approach aimed to reproduce published analyses as faithfully as possible, using the details provided in the original papers. We acknowledge that other statisticians may have differing success in reproducing results due to variations in data handling and implicit methodological choices not fully described in publications. However, we maintain that research articles should contain sufficient detail for any qualified statistician to reproduce the analyses independently.

Methods used in our reproducibility analyses

There were two parts to our study. First, 100 articles published in PLOS ONE were randomly selected from the health domain and sent for post-publication peer review by statisticians. Of these, 95 included linear regression analyses and were therefore assessed for reporting quality. The statisticians evaluated what was reported, including regression coefficients, 95% confidence intervals, and p-values, as well as whether model assumptions were described and how those assumptions were evaluated. This report provides a brief summary of the initial statistical review.

The second part of the study involved reproducing linear regression analyses for papers with available data to assess both computational and inferential reproducibility. All papers were initially assessed for data availability, and the statistical software used. From those with accessible data, the first 20 papers (from the original random sample) were evaluated for computational reproducibility. Within each paper, individual linear regression models were identified and assigned a unique number. A maximum of three models per paper were selected for assessment. When more than three models were reported, priority was given to the final model or the primary models of interest as identified by the authors; any remaining models were selected at random.

To assess computational reproducibility, differences between the original and reproduced results were evaluated using absolute discrepancies and rounding error thresholds, tailored to the number of decimal places reported in each paper. Results for each reported statistic, e.g., regression coefficient, were categorised as Reproduced, Incorrect Rounding, or Not Reproduced, depending on how closely they matched the original values. Each paper was then classified as Reproduced, Mostly Reproduced, Partially Reproduced, or Not Reproduced. The mostly reproduced category included cases with minor rounding or typographical errors, whereas partially reproduced indicated substantial errors were observed, but some results were reproduced.

For models deemed at least partially computationally reproducible, inferential reproducibility was further assessed by examining whether statistical assumptions were met and by conducting sensitivity analyses, including bootstrapping where appropriate. We examined changes in standardized regression coefficients, which reflect the change in the outcome (in standard deviation units) for a one standard deviation increase in the predictor. Meaningful differences were defined as a relative change of 10% or more, or absolute differences of 0.1 (moderate) and 0.2 (substantial). When non-linear relationships were identified, inferential reproducibility was assessed by comparing model fit measures, including R², Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). When the Gaussian distribution was not appropriate for the dependent variable, alternative distributions were considered, and model fit was evaluated using AIC and BIC.

Summary from statistical review

This paper explored stigma attitudes, mental illness attitudes, and attitudes toward the adoption of evidence-based practice among health care providers in Bahrain. Linear regression was the main analysis, with the authors describing it using a full multivariable model. Then, the model was redefined to include only significant variables: no assumptions, collinearity, or outliers were discussed. Data was visualised using a scatterplot. The strength of this paper was that the regression output was shown, including all statistics, although it was copied and pasted from SPSS. The main weakness was the lack of interpretation of regression coefficients, with an over-reliance on statistical significance. The degrees of freedom indicated that variables in the regression analyses were treated as either continuous or dichotomous. However, this was not clearly explained, and several variables presented in the tables appeared to have more than two categories.

Data availability and software used

The data were available in a wide Excel format from Dryad, with no accompanying data dictionary. Data was analysed using SPSS.

Regression sample

Data were available but were not assessed for reproducibility, as reproduction was limited to the first 20 papers with accessible datasets.

Computational reproducibility results

Data were available but were not assessed for computational reproducibility, as reproduction was limited to the first 20 papers with accessible datasets.

Inferential reproducibility results

Data were available but were not assessed for inferential reproducibility, as reproduction was limited to the first 20 papers with accessible datasets.