{"doc_desc":{"title":"Bayesian latent class modelling to examine the diagnostic accuracy of the first hetero-assessment instrument for occupational burnout","idno":"10.16909-DATASET-35","producers":[{"name":"Center for Primary Care and Public Health (Unisant\u00e9), University of Lausanne, Switzerland","abbreviation":"Unisant\u00e9","affiliation":"","role":"Data publisher"}],"version_statement":{"version":"Version 1.0 (January 2023)"}},"study_desc":{"title_statement":{"idno":"10.16909-DATASET-35","title":"Bayesian latent class modelling to examine the diagnostic accuracy of the first hetero-assessment instrument for occupational burnout"},"authoring_entity":[{"name":"Guseva Canu, Irina","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), Department of occupational and environmental health (DSTE)"},{"name":"Shoman, Yara","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), Department of occupational and environmental health (DSTE)"}],"production_statement":{"producers":[{"name":"Hartnack, Sonja","affiliation":"Section of Epidemiology, Vetsuisse Faculty, University of Zurich, 8057 Zurich, Switzerland","role":"Statistical analysis and co-author of the manuscript"},{"name":"Leclercq, C\u00e9line","affiliation":"Human Resources Development Unit, Faculty of Psychology and Education Sciences, University of Li\u00e8ge, 4000 Li\u00e8ge, Belgium","role":"Co-author of the manuscript"},{"name":"Hansez, Isabelle","affiliation":"Human Resources Development Unit, Faculty of Psychology and Education Sciences, University of Li\u00e8ge, 4000 Li\u00e8ge, Belgium","role":"Co-author of the manuscript"}],"copyright":"(c) 2023, Center for Primary Care and Public Health (Unisant\u00e9), University of Lausanne, Switzerland","funding_agencies":[{"name":"The European Union\u2019s Horizon 2020 research and innovation program (under the Marie Sk\u0142odowska-Curie grant agreement No 801076, through the SSPH+ Global PhD Fellowship Program in Public Health Sciences (GlobalP3HS) of the Swiss School of Public Health) partly supported the PhD position of YS.","abbreviation":"SSPH+ Global PhD","role":""},{"name":"Unisant\u00e9 supported the other part, via the General Directorate of Health of the Canton of Vaud via the grant of the Commission for Health Promotion and the Fight against Addictions Grant N\u25e68273\/3636000000-801.","abbreviation":"","role":""}]},"distribution_statement":{"distributors":[{"name":"Center for Primary Care and Public Health (Unisant\u00e9), University of Lausanne, Switzerland","abbreviation":"Unisant\u00e9","affiliation":"UNIL","uri":"https:\/\/www.unisante.ch"}]},"study_info":{"keywords":[{"keyword":"Burnout","vocab":"","uri":""},{"keyword":"Bayesian analysis","vocab":"","uri":""},{"keyword":"R scripts","vocab":"","uri":""}],"abstract":"Occupational burnout has no standardized diagnostic or screening criteria. Following a dozen of patient-reported outcome measures (PROMs) for occupational burnout, Belgian researchers developed the first hetero-assessment instrument (HAI) designed for health professionals\u2019 use. The HAI\u2019s sensitivity and specificity was previously assessed with reference to the OLdenburg Burnout Inventory (OLBI) using frequentist statistics in Belgium (100 participants) and Switzerland (42 participants). This study aimed at assessing the HAI\u2019s diagnostic performance using Bayesian latent class modelling (BLCM). We applied Hui-Walter framework for two tests and two populations and ran models with minimally informative priors, with and without conditional dependency between HAI and OLBI results. We further performed sensitivity analysis by replacing one of the minimally informative priors by the distribution beta (2,1) at each time for all priors. We also performed the analysis using literature-based informative priors for OLBI. Using the BLCM without conditional dependency, the sensitivity and specificity of the HAI was 0.91 (0.77-1.00) and 0.82 (0.59-1.00), respectively. The sensitivity analysis did not yield any significant changes in these results. In all models, the sensitivity was never below 0.82 and the specificity was never below 0.78. The HAI\u2019s sensitivity and specificity determined in this study are better compared to the previous studies conducted using frequentist statistics. These finding suggests that the use of BLCM is preferred in the absence of the diagnostic gold standard and precludes underestimating the diagnostic accuracy of the tested instrument.","coll_dates":[{"start":"2010-01-01","end":"2013-12-31","cycle":"for Swiss data"},{"start":"2019-01-01","end":"2019-12-31","cycle":"for Belgian data"}],"nation":[{"name":"Switzerland","abbreviation":"CHE"},{"name":"Belgium","abbreviation":"BE"}],"geog_coverage":"Switzerland and Belgium","analysis_unit":"The unit of analysis is the individual person","universe":"Patients from medical consultations with general practitioners (GP) and occupational physicians (OP)","data_kind":"Secondary data in the form of tables based on the analysis of de-identified patient data. The tables were taken from other published articles.\n\t\t\tMaterial made available are composed by tables (image in PDF) and R script."},"method":{"data_collection":{"sampling_procedure":"For the Swiss study: a convenience sample of patients received at the Unisant\u00e9 \u201cWork and Suffering\u201d Consultation (WSC) between 2010 and 2013. WSC patients for whom a completed OLBI was available in their medical record along with the WSC detailed report were included.\n\t\t\t\tFor the Belgian study: The target population concerns people who have consulted a GP or an OP and who have expressed complaints and symptoms of suffering at work. Patients who filled OLBI and their clinical judgement using HAI can be linked were included.","coll_mode":"Data were taken from 2 published articles (2 tables available in image). Primary data were collected in the process of a medical consultation through questionnaires : one self reported and on filled in by the physician.","cleaning_operations":"No cleaning operation, data has been directly analyzed."},"analysis_info":{"data_appraisal":"No action necessary to appraise data."}},"data_access":{"dataset_use":{"contact":[{"name":"Guseva Canu, Irina","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), Department of occupational and environmental health (DSTE)","email":"irina.guseva-canu@unisante.ch","uri":""},{"name":"Racine, C\u00e9line (Repository manager)","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), University of Lausanne, Switzerland","email":"udd.data@unisante.ch","uri":""}],"cit_req":"Shoman Y, Hartnack S, Leclercq C, Hansez I, Guseva Canu I. Bayesian latent class modelling to examine the diagnostic accuracy of the first hetero-assessment instrument for occupational burnout. Center for Primary Care and Public Health (Unisant\u00e9), University of Lausanne, Switzerland. Version 1.0 of the licensed dataset (01\/2023), provided by the Unisant\u00e9 Research Data Repository. DOI:10.16909\/DATASET\/35","disclaimer":"The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses."}}},"schematype":"survey"}