Giorgio Oronzo Spagnolo

Second victim syndrome (SVS) refers to the psychological trauma experienced by healthcare workers (HCWs) as a result of being involved in an adverse event (AE). Research on the prevalence of SVS and the support needed for HCWs who experience it is limited. A cross-sectional study was conducted at the Health Local Unit of Lecce, in Puglia, to identify the phenomenon of SVS among HCWs and recognize the forms of support received and desired. A validated questionnaire, IT-SVEST, was administered to doctors and nurses. The survey received responses from 250 HCWs, and 41% of respondents reported being involved in an AE that could cause SVS. Among the seven dimensions measuring the effects of the SVS and two outcome variables, the highest percentage of agreement was found for psychological distress (23.5%), followed by turnover intentions (19.8%) and physical distress (9.9%); 23.8% of the interviewees declared that they did not receive institutional support, and 9.9% identified help resources mostly in non-work-related support (9.9%), followed by supervisor support (9.3%). The multivariable binary logistic regression analysis showed a positive association between the occurrence of an AE and the medical doctor profession (OR = 4.267, p ≤ 0.0001), and affiliation to intensive care departments (OR = 5.133, p ≤ 0.0001) and male gender (OR = 2.069, p = 0.033). SVS is a serious problem that affects the entire health system, systematic surveys and appropriate institutional responses including formal support programs for affected HCWs are a priority.

Purpose: Business processes (BPs) of public administrations (PAs) are often described in the form of written procedures or operational manuals. These business process descriptions are expected to be properly understood and applied by civil servants, who have to provide legally compliant service provisions to the citizens. However, process descriptions in the PA are sometimes hard to read, ambiguous, or vague, leading to false interpretations or even incorrect execution of the processes. The purpose of this paper is to focus on improving the descriptions of BPs to be used in PAs. Design/methodology/approach: To this end, the authors present an in-depth domain analysis, including a literature review and interviews with PA stakeholders belonging to different realities. From this analysis, the authors identified a set of 52 typical defects of process descriptions. Findings: The authors provide a set of guidelines and a template to constrain the definition of these documents and to mitigate the identified defects. Furthermore, the authors outline research challenges in the field of quality assessment of textual process descriptions for the PA. Research limitations/implications: This paper addresses the needs of any PA officer who has to write an official procedure or operational manual, and should be studied by researchers who wish to provide automatic strategies to check the quality of these documents. Originality/value: Text quality issues have been addressed in various fields (e.g. requirements engineering), but not in the area of BP descriptions of PAs. The contribution consists in the study of the quality issues that occur and create problems in the practice of this particular domain. Based on this insight, we provide directions for research that will find solutions to mitigate the issues.

This paper presents PURE (PUblic REquirements dataset), a dataset of 79 publicly available natural language requirements documents collected from the Web. The dataset includes 34,268 sentences and can be used for natural language processing tasks that are typical in requirements engineering, such as model synthesis, abstraction identification and document structure assessment. It can be further annotated to work as a benchmark for other tasks, such as ambiguity detection, requirements categorisation and identification of equivalent re-quirements. In the paper, we present the dataset and we compare its language with generic English texts, showing the peculiarities of the requirements jargon, made of a restricted vocabulary of domain-specific acronyms and words, and long sentences. We also present the common XML format to which we have manually ported a subset of the documents, with the goal of facilitating replication of NLP experiments.