Development and implementation of fidelity assessment in first episode psychosis services in Czechia: A pilot study.

  • Published In: Early Intervention in Psychiatry, 2023, v. 17, n. 6. P. 573 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Tomaskova, Hana; Kondrátová, Lucie; Winkler, Petr; Addington, Donald 3 of 3

Abstract

Aim: The aim of the study was to evaluate fidelity in first episode psychosis (FEP) teams in Czechia and to gage the feasibility and utility of the process in a mental health system that is undergoing a transformation. Methods: Fidelity assessment was conducted using The First Episode Psychosis Services Fidelity Scale (FEPS‐FS). Fidelity assessment was based on a review of data abstracted from the health records of active clients, program documents, administrative data, and interviews with members of staff. The mean scores were compared across the teams. Feasibility and utility were assessed by program response to their fidelity results. Results: Three FEP teams were involved in the fidelity assessment. Across the 35 items, the mean fidelity score ranged from 2.5 to 3.1. Across the FEP teams, the percentage of the 35 items rated as 4 or 5 (satisfactory or exemplary) ranged from 34.3% to 51.4%. Conclusions: This study provided an opportunity to implement FEPS‐FS and assess fidelity in FEP teams in Czechia. The fidelity assessment also provided a baseline for measuring change. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Early Intervention in Psychiatry. 2023/06, Vol. 17, Issue 6, p573
  • Document Type:Article
  • Subject Area:Geography and Cartography
  • Publication Date:2023
  • ISSN:1751-7885
  • DOI:10.1111/eip.13350
  • Accession Number:164136690
  • Copyright Statement:Copyright of Early Intervention in Psychiatry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.