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ZENODO
Dataset . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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A Theoretical Framework for Hybrid Deterministic SIRS Modeling and Bayesian Hierarchical Inference in Burnout Propagation in Medical Education: Calibrated Simulations, Risk Factor Analysis, and Intervention Projections

Authors: Shibah, Sami Rashid Mohammed;

A Theoretical Framework for Hybrid Deterministic SIRS Modeling and Bayesian Hierarchical Inference in Burnout Propagation in Medical Education: Calibrated Simulations, Risk Factor Analysis, and Intervention Projections

Abstract

Background: Burnout affects approximately 37-56% of medical undergraduates globally, escalating to around 44% immediately prior to residency and posing a substantial threat to healthcare workforce sustainability.Methods: This theoretical manuscript introduces a hybrid modeling framework that integrates a deterministic Susceptible-Infected-Recovered-Susceptible (SIRS) model of burnout contagion with Bayesian hierarchical inference for risk factor analysis, informed by meta-analytic priors. Key calibrated parameters encompass the initial prevalence 0.3723, weekly transmission rate 0.05 (derived from longitudinal escalation patterns), recovery rate 0.02 (reflecting mindfulness intervention effects, standardized mean difference = -0.42), and relapse rate 0.01. Hierarchical priors incorporate empathy-burnout correlations (effect size r = -0.15) and stress-related coefficients (0.39). Markov chain Monte Carlo (MCMC) posteriors, based on 10,000 iterations, are estimated. Sensitivity analysis via parameter sweeps and Monte Carlo simulations (with parameters drawn from normal distributions: \( \beta \sim \mathcal{N}(0.05, 0.005) \), \( \gamma \sim \mathcal{N}(0.02, 0.002) \), \( \delta \sim \mathcal{N}(0.01, 0.001) \)) are performed.Results: Sensitivity analysis demonstrates that variations in stress levels and transmission rates significantly influence peak burnout prevalence, with posteriors estimating stress coefficient 0.40 (95% highest density interval: 0.35--0.45) and empathy -0.16 (95% highest density interval: -0.21-- -0.11). Parameter sweeps attribute 85% of peak prevalence variance to transmission rate, while Monte Carlo simulations yield 95% prediction intervals of 380--485 cases for a cohort of 1000. Posterior predictive checks (p = 0.07) validate model fit to observed empirical peaks (45% in year 3). Projected interventions, such as pass/fail grading (odds ratio = 1.4, modeled as \( \beta \to 0.04 \)) averts ~8% of peak cases (387 vs. 422), and mindfulness training (standardized mean difference = -0.42, modeled as \( \gamma \to 0.025 \)) averts ~8% of peak cases (387 cases).Conclusions: This framework promotes ethical, evidence-based strategies for burnout prevention across preclinical and residency training phases, with extensible applications to other social epidemics, including depression and anxiety contagion through SEIR model variants.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average