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April 1, 20260 citationsOpen Access

La Profilée — A Structural Prediction for the Maslach Burnout Inventory

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MMMarc Maibom

Key Points

  • The research aims to derive structural predictions for the dimensions of the Maslach Burnout Inventory using first principles.
  • Established a multiplicative condition IK = F · I · C for burnout dynamics.
  • Identified correlations between MBI dimensions Emotional Exhaustion, Depersonalisation, and Personal Accomplishment.
  • Developed four predictions based on structural properties of the multiplicative model.
  • Analyzed existing longitudinal datasets of the MBI for empirical testing.
  • Prediction P1: Models incorporating interaction between EE and DP outperform additive models in high-burnout instances.
  • Prediction P2: A structural breakpoint is detectable in the EE-PA relationship.
  • Prediction P3: Identified a directional lag structure between the onset of EE, DP, and PA collapse.
  • Prediction P4: Found differences in intervention effectiveness based on distinct types of system collapse.

Abstract

La Profilée (LP) establishes a multiplicative persistence condition IK = F · I · C, where F, I, and C are structurally necessary components of any persisting system. Prior publications have shown that the three dimensions of the Maslach Burnout Inventory (MBI) — Emotional Exhaustion (EE), Depersonalisation/Cynicism (DP), and reduced Personal Accomplishment (PA) — inadvertently proxy the LP variables I, F·C, and IK respectively. The MBI literature documents the sequence and correlation structure of these dimensions but has not derived structural predictions about their collapse dynamics from first principles. This paper derives four testable predictions that follow necessarily from the multiplicative structure of IK. These predictions have not been made by any existing burnout framework, differ from what correlation structure alone implies, and are testable against existing longitudinal MBI datasets. They are submitted as prospective structural claims prior to empirical testing. The four predictions concern: (P1) interaction dominance in PA variance — models including EE×DP must outperform additive models, especially in high-burnout regimes; (P2) a detectable structural breakpoint in the EE–PA relationship consistent with the IR = 1 threshold; (P3) a specific directional lag structure between EE onset, DP onset, and PA collapse; and (P4) a crossover interaction in intervention efficacy between structurally distinct collapse types. Taken jointly, P1–P4 form a pattern that no standard additive or latent-factor model can reproduce.

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Cite This Study

Marc Maibom (2026) studied this question.

synapsesocial.com/papers/69ccb79916edfba7beb89b06https://doi.org/10.5281/zenodo.19339368
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