Assessment of search strategies in Medline to identify studies on the impact of long COVID on workability.

MEDLINE bibliometrics information retrieval methods long COVID work

Journal

Frontiers in research metrics and analytics
ISSN: 2504-0537
Titre abrégé: Front Res Metr Anal
Pays: Switzerland
ID NLM: 101718019

Informations de publication

Date de publication:
2024
Historique:
received: 21 11 2023
accepted: 19 02 2024
medline: 18 3 2024
pubmed: 18 3 2024
entrez: 18 3 2024
Statut: epublish

Résumé

Studies on the impact of long COVID on work capacity are increasing but are difficult to locate in bibliographic databases, due to the heterogeneity of the terms used to describe this new condition and its consequences. This study aims to report on the effectiveness of different search strategies to find studies on the impact of long COVID on work participation in PubMed and to create validated search strings. We searched PubMed for articles published on Long COVID and including information about work. Relevant articles were identified and their reference lists were screened. Occupational health journals were manually scanned to identify articles that could have been missed. A total of 885 articles potentially relevant were collected and 120 were finally included in a gold standard database. Recall, Precision, and Number Needed to Read (NNR) of various keywords or combinations of keywords were assessed. Overall, 123 search-words alone or in combination were tested. The highest Recalls with a single MeSH term or textword were 23 and 90%, respectively. Two different search strings were developed, one optimizing Recall while keeping Precision acceptable (Recall 98.3%, Precision 15.9%, NNR 6.3) and one optimizing Precision while keeping Recall acceptable (Recall 90.8%, Precision 26.1%, NNR 3.8). No single MeSH term allows to find all relevant studies on the impact of long COVID on work ability in PubMed. The use of various MeSH and non-MeSH terms in combination is required to recover such studies without being overwhelmed by irrelevant articles.

Identifiants

pubmed: 38495828
doi: 10.3389/frma.2024.1300533
pmc: PMC10940504
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1300533

Informations de copyright

Copyright © 2024 Gehanno, Thaon, Pelissier and Rollin.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Auteurs

Jean-François Gehanno (JF)

Institute of Occupational Medicine, Rouen University Hospital, Rouen, France.
Inserm, Rouen University, Sorbonne University, University of Paris 13, Laboratory of Medical Informatics and Knowledge Engineering in e-Health, LIMICS, Paris, France.

Isabelle Thaon (I)

Centre de Consultations de Pathologie Professionnelle, CHRU de Nancy, Vandoeuvre les Nancy, Nancy, France.

Carole Pelissier (C)

Centre Hospitalier Universitaire de Saint-Etienne, Université Lyon 1, Université de St Etienne, Université Gustave Eiffel-IFSTTAR, Saint-Etienne, France.
UMRESTTE UMR-T9405, Saint-Etienne, France.

Laetitia Rollin (L)

Institute of Occupational Medicine, Rouen University Hospital, Rouen, France.
Inserm, Rouen University, Sorbonne University, University of Paris 13, Laboratory of Medical Informatics and Knowledge Engineering in e-Health, LIMICS, Paris, France.

Classifications MeSH