FROM STATISTICIAN-DEPENDENT PRACTICE TO DATA-ANALYTIC AGENCY: AN ACTIVITY-THEORETICAL CASE FOR TRAINING VETERINARY AND HEALTH PROFESSIONALS AS DATA ANALYSTS IN THE AGE OF AI

A SYSTEMIC-STRUCTURAL ACTIVITY THEORY ARGUMENT FOR INTEGRATED DATA WORK IN VETERINARY MEDICINE, ANIMAL HUSBANDRY, AND HEALTH SCIENCES

Authors

DOI:

https://doi.org/10.29121/JISSI.v2.i2.2026.35

Keywords:

Activity Theory, Systemic-Structural Activity Theory, Data Literacy, Veterinary Informatics, Rstudio, Quarto, Reproducible Research, Machine Learning, Large Language Models

Abstract

Veterinary medicine, animal husbandry, and health sciences operate in data-saturated ecologies shaped by electronic records, sensor systems, laboratory platforms, and increasingly automated analytic infrastructures. In this context, the dominant educational dilemma is not whether professionals should be statistically literate, but whether they should remain dependent on specialist analysts for the core analytic actions that define contemporary evidence-based practice, production optimization, and welfare governance. This article argues that training veterinarians, zootechnists, and health professionals as data analysts is superior to training them primarily as statisticians or as researchers who routinely outsource analytic work. The argument is grounded in cultural-historical activity theory and Bedny’s systemic-structural activity theory, and it is updated for a technical environment in which machine learning, large language models, and agentic workflows reduce the friction of coding and documentation while increasing the need for domain-grounded judgement. R, RStudio, and Quarto are presented as an exemplary toolchain that unifies analysis, explanation, and publication into a single reproducible activity. The conclusion proposes a competence-oriented curriculum in which communicative, transformative, and evaluative capabilities co-develop through authentic analytic work tied to welfare and community responsibility.

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Published

2026-08-06

How to Cite

FROM STATISTICIAN-DEPENDENT PRACTICE TO DATA-ANALYTIC AGENCY: AN ACTIVITY-THEORETICAL CASE FOR TRAINING VETERINARY AND HEALTH PROFESSIONALS AS DATA ANALYSTS IN THE AGE OF AI: A SYSTEMIC-STRUCTURAL ACTIVITY THEORY ARGUMENT FOR INTEGRATED DATA WORK IN VETERINARY MEDICINE, ANIMAL HUSBANDRY, AND HEALTH SCIENCES. (2026). Journal of Integrative Science and Societal Impact, 2(2), 23-28. https://doi.org/10.29121/JISSI.v2.i2.2026.35