Bioethics in Primary Health Care: A Bayesian Approach to Conceptual Dichotomy

Background Graduate Medical Education (GME) programs frequently generate crucial evaluative micro-data from small programmatic cohorts. Traditional frequentist statistics often discard these datasets as “underpowered,” leaving critical curricular gaps undetected. This study demonstrates how Bayesian inference can rescue small-sample educational data to extract robust pedagogical signals. Methods As a methodological proof of concept, we conducted a Bayesian re-analysis of a historical cross-sectional bioethics evaluation involving a small cohort of Primary Health Care teaching faculty (n=12). A Bayesian Binomial Test with a non-informative Jeffreys prior was employed to calculate the Bayes Factor (BF) and quantify the exact strength of evidence. Evidence was interpreted according to the standard JASP classification guidelines. Results The Bayesian model extracted high-certainty signals from the historical micro-data. It revealed a stark conceptual dichotomy: Moderate Evidence (BF +0 = 8.13) of theoretical mastery in Beneficence, juxtaposed with Extreme Evidence (BF 0+ = 1320.3) of a complete deficit in the practical application of Non-Maleficence (Prudence). Conclusions Bayesian inference provides GME leadership with a robust mathematical tool to evaluate small residency and faculty cohorts. By rescuing evaluative micro-data, the model empirically confirms that theoretical teaching does not automatically translate into prudential clinical judgment, justifying targeted curricular interventions even when sample sizes are minimal.