Top-of-License, Bottom-of-Wellbeing? Identity-Adjusted Returns to Automation in Public and Health Services

Public-sector automation is often evaluated via potential task substitution under data constraints (Savignon et al. 2023). In health pathways, Khavandi et al. (2023) plan to study how an autonomous telemedicine assistant (Dora) shifts tasks so clinicians “work at the top of their license.” But we lack a framework that connects these task reallocations to professional identity, wellbeing, and retention—especially where relational work intensifies (echoing Moradi et al. 2024). We propose a step-wedge, multi-site evaluation of conversational AI rollouts in public and health services, combining admin logs, time-use shadowing, and psychometric measures. The theory augments the production function with identity utility: automation that strips routine tasks may raise measured productivity while imposing identity dissonance costs and altering career ladders. We test heterogeneous effects across seniority and gender and link to occupational mobility under uninsured risk (Faia, Shabalina, & Kudlyak 2021), asking whether identity costs dampen mobility toward newly emerging tasks. Novelty: moving beyond automatable-task counts to an identity-adjusted welfare metric and equity lens for public jobs, in line with Savignon et al.’s call for granular maps under data scarcity. The impact is a richer cost–benefit calculus for public technology choices that anticipates resistance, burnout, and skill pipeline risks.

References:

1. Dynamic Labor Reallocation with Heterogeneous Skills and Uninsured Idiosyncratic Risk. Ester Faia, Ekaterina Shabalina, Marianna Kudlyak (2021). Federal Reserve Bank of San Francisco, Working Paper Series.
2. Automation in public sector jobs and services: a framework to analyze public digital transformation’s impact in a data-constrained environment. Andrea Bonomi Savignon, Riccardo Zecchinelli, Lorenzo Costumato, Fabiana Scalabrini (2023). Transforming Government: People, Process and Policy.
3. Pseudo-Automation: How Labor-Offsetting Technologies Reconfigure Roles and Relationships in Frontline Retail Work. Pegah Moradi, Karen Levy, Cristobal Cheyre (2024). Proc. ACM Hum. Comput. Interact..
4. Investigating the Impact of Automation on the Health Care Workforce Through Autonomous Telemedicine in the Cataract Pathway: Protocol for a Multicenter Study. Sarah Khavandi, Fatema Zaghloul, A. Higham, Ernest Lim, N. de Pennington, L. Celi (2023). JMIR Research Protocols.