The Three-Step Workflow: A Pragmatic Approach to Allocating Academic Hospitals’ Affiliations for Bibliometric Purposes

doi:10.2478/jdis-2022-0006

Abstract

Abstract:

Purpose: A key question when ranking universities is whether or not to allocate the publication output of affiliated hospitals to universities. This paper presents a method for classifying the varying degrees of interdependency between academic hospitals and universities in the context of the Leiden Ranking.
Design/methodology/approach: Hospital nomenclatures vary worldwide to denote some form of collaboration with a university, however they do not correspond to universally standard definitions. Thus, rather than seeking a normative definition of academic hospitals, we propose a three-step workflow that aligns the university-hospital relationship with one of three general models: full integration of the hospital and the medical faculty into a single organization; health science centres in which hospitals and medical faculty remain separate entities albeit within the same governance structure; and structures in which universities and hospitals are separate entities which collaborate with one another. This classification system provides a standard through which publications which mention affiliations with academic hospitals can be better allocated.
Findings: In the paper we illustrate how the three-step workflow effectively translates the three above-mentioned models into two types of instrumental relationships for the assignation of publications: “associate” and “component”. When a hospital and a medical faculty are fully integrated or when a hospital is part of a health science centre, the relationship is classified as component. When a hospital follows the model of collaboration and support, the relationship is classified as associate. The compilation of data following these standards allows for a more uniform comparison between worldwide educational and research systems.
Research limitations: The workflow is resource intensive, depends heavily on the information provided by universities and hospitals, and is more challenging for languages that use non-Latin characters. Further, the application of the workflow demands a careful evaluation of different types of input which can result in ambiguity and makes it difficult to automatize.
Practical implications: Determining the type of affiliation an academic hospital has with a university can have a substantial impact on the publication counts for universities. This workflow can also aid in analysing collaborations among the two types of organizations.
Originality/value: The three-step workflow is a unique way to establish the type of relationship an academic hospital has with a university accounting for national and regional differences on nomenclature.

Key words: Academic hospitals, Universities, Bibliometrics, Organizational affiliations, Rankings

Reyes Elizondo, Andrea, Clara Calero-Medina, and Martijn S. Visser. "The Three-Step Workflow: A Pragmatic Approach to Allocating Academic Hospitals’ Affiliations for Bibliometric Purposes." Journal of Data and Information Science, vol.7, no.1, 2022, pp.20-36. DOI: 10.2478/jdis-2022-0006

Figures/Tables 5

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Table 1

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Organization	Separate output	Combined output	Percentage
Université de Tours	3,463	4,525	31%
Centre Hospitalier Régional Universitaire de Tours	1,062	4,525	31%
University of Zurich	18,573	22,297	20%
University Hospital Zurich	3,724	22,297	20%
University of Parma	5,222	5,924	13%
Azienda Ospedaliero-Universitaria di Parma	702	5,924	13%
Complutense University of Madrid	12,908	14,300	11%
Hospital Clínico San Carlos	1,392	14,300	11%
University of Ottawa	14,959	16,474	10%
The Ottawa Hospital	949
Children's Hospital of Eastern Ontario	566
University of Ioannina	3,479	3,689	6%
University Hospital of Ioannina	210	3,689	6%
University of Turku	7,849	8,195	4%
Turku University Hospital	346	8,195	4%
Palacký University Olomouc	4497	4,620	3%
University Hospital Olomouc	123	4,620	3%
Australian National University	13,932	14,158	2%
Canberra Hospital	226	14,158	2%
University of Hong Kong	17,790	18,015	1%
Queen Mary Hospital, Hong Kong	225	18,015	1%
University of Buenos Aires	8,434	8,492	1%
Hospital de Clínicas José de San Martín	58	8,492	1%
University of New Mexico	8,138	8,179	0%
UNM Health Sciences Center	41	8,179	0%