Commentators have recommended that forensic scientists\' reports contain various disclosures to facilitate comprehension. However, little research has explored whether following best practice recommendations for disclosure impacts on receivers\' impressions of the evidence. We examined whether forensic science reports that are more compliant with these best practice recommendations reduced overvaluing of the evidence and sensitized legal and community decision-makers to evidence quality. Across three experiments, 240 legal practitioners/trainees and 566 community decision-makers were presented with a fingerprint or footwear report that was either compliant or non-compliant with best practice recommendations. Participants were then asked to make evaluations and decisions based on the report. We found mixed effects of report compliance. Report compliance affected community participant\'s evaluations of the persuasiveness of the evidence but had limited impact on the judgments of legal practitioners/trainees. When presented with compliant reports, we found that community participants regarded unknown reliability evidence as less reliable and less persuasive than high reliability evidence, suggesting disclosures helped reduce overvaluing of the evidence and create sensitivity to differences in evidence quality. These results suggest compliance with reporting recommendations does affect community impressions, while only minimally influencing legal impressions of forensic science evidence. The costs and/or benefits of this outcome require further examination.

The sizeable body of evidence indicating that parenting programs have a positive impact on children and families highlights the potential public health benefits of their implementation on a large scale. Despite evidence and global attention, beyond the highly controlled delivery of parenting programs via randomized trials, little is known about program effectiveness or how to explain the poorer results commonly observed when implemented in community settings. Researchers, practitioners, and policymakers must work together to identify what is needed to spur adoption and sustainment of evidence-based parenting programs in real-world service systems and how to enhance program effectiveness when delivered via these systems. Collecting, analyzing, and using facilitator fidelity data is an important frontier through which researchers and practitioners can contribute. In this commentary, we outline the value of assessing facilitator fidelity and utilizing the data generated from these assessments; describe gaps in research, knowledge, and practice; and recommend directions for research and practice. In making recommendations, we describe a collaborative process to develop a preliminary guideline-the Fidelity of Implementation in Parenting Programs Guideline or FIPP-to use when reporting on facilitator fidelity. Readers are invited to complete an online survey to provide comments and feedback on the first draft of the guideline.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s43477-023-00092-5.

Natural language processing (NLP) can be used to process and structure free text, such as (free text) radiological reports. In radiology, it is important that reports are complete and accurate for clinical staging of, for instance, pulmonary oncology. A computed tomography (CT) or positron emission tomography (PET)-CT scan is of great importance in tumor staging, and NLP may be of additional value to the radiological report when used in the staging process as it may be able to extract the T and N stage of the 8th tumor-node-metastasis (TNM) classification system. The purpose of this study is to evaluate a new TN algorithm (TN-PET-CT) by adding a layer of metabolic activity to an already existing rule-based NLP algorithm (TN-CT). This new TN-PET-CT algorithm is capable of staging chest CT examinations as well as PET-CT scans. The study design made it possible to perform a subgroup analysis to test the external validation of the prior TN-CT algorithm. For information extraction and matching, pyContextNLP, SpaCy, and regular expressions were used. Overall TN accuracy score of the TN-PET-CT algorithm was 0.73 and 0.62 in the training and validation set (N = 63, N = 100). The external validation of the TN-CT classifier (N = 65) was 0.72. Overall, it is possible to adjust the TN-CT algorithm into a TN-PET-CT algorithm. However, outcomes highly depend on the accuracy of the report, the used vocabulary, and its context to express, for example, uncertainty. This is true for both the adjusted PET-CT algorithm and for the CT algorithm when applied in another hospital.

BACKGROUND: Adverse events refer to incidents with potential or actual harm to patients in hospitals. These events are typically documented through patient safety event (PSE) reports, which consist of detailed narratives providing contextual information on the occurrences. Accurate classification of PSE reports is crucial for patient safety monitoring. However, this process faces challenges due to inconsistencies in classifications and the sheer volume of reports. Recent advancements in text representation, particularly contextual text representation derived from transformer-based language models, offer a promising solution for more precise PSE report classification. Integrating the machine learning (ML) classifier necessitates a balance between human expertise and artificial intelligence (AI). Central to this integration is the concept of explainability, which is crucial for building trust and ensuring effective human-AI collaboration.
OBJECTIVE: This study aims to investigate the efficacy of ML classifiers trained using contextual text representation in automatically classifying PSE reports. Furthermore, the study presents an interface that integrates the ML classifier with the explainability technique to facilitate human-AI collaboration for PSE report classification.
METHODS: This study used a data set of 861 PSE reports from a large academic hospital\'s maternity units in the Southeastern United States. Various ML classifiers were trained with both static and contextual text representations of PSE reports. The trained ML classifiers were evaluated with multiclass classification metrics and the confusion matrix. The local interpretable model-agnostic explanations (LIME) technique was used to provide the rationale for the ML classifier\'s predictions. An interface that integrates the ML classifier with the LIME technique was designed for incident reporting systems.
RESULTS: The top-performing classifier using contextual representation was able to obtain an accuracy of 75.4% (95/126) compared to an accuracy of 66.7% (84/126) by the top-performing classifier trained using static text representation. A PSE reporting interface has been designed to facilitate human-AI collaboration in PSE report classification. In this design, the ML classifier recommends the top 2 most probable event types, along with the explanations for the prediction, enabling PSE reporters and patient safety analysts to choose the most suitable one. The LIME technique showed that the classifier occasionally relies on arbitrary words for classification, emphasizing the necessity of human oversight.
CONCLUSIONS: This study demonstrates that training ML classifiers with contextual text representations can significantly enhance the accuracy of PSE report classification. The interface designed in this study lays the foundation for human-AI collaboration in the classification of PSE reports. The insights gained from this research enhance the decision-making process in PSE report classification, enabling hospitals to more efficiently identify potential risks and hazards and enabling patient safety analysts to take timely actions to prevent patient harm.

OBJECTIVE: To examine preferences of veterinary clinical pathologists, clinicians, and students for cytology report formats.
METHODS: 24 clinical pathologists, 1,014 veterinarians, and 93 veterinary students who were members of the Veterinary Information Network.
METHODS: Members of the Veterinary Information Network responded to an online survey invitation, made available between July 11, 2023, and July 24, 2023. Respondents were randomly directed to 1 of 4 sets of cytology reports, each containing a traditional narrative format, narrative format with terms expressing a degree of confidence and associated numerical ranges, and template format with similar estimates of confidence. Respondents ranked the reports in order of preference and then provided comments about their top-ranked choice. Responses were analyzed mostly with descriptive statistics or comparisons of proportions.
RESULTS: 14 of 24 clinical pathologists preferred the traditional narrative format, whereas 449 of 1,042 veterinary clinicians and veterinary students preferred the template format. Respondents (460/1,131) ranked the template format as most preferred, but the narrative format with terms expressing a degree of confidence ranked highest overall. Many respondents appeared to misunderstand the degree of confidence estimates being expressed numerically. Respondents choosing each format often stated that their preferred choice was \"easiest to understand\" and \"most comprehensive.\"
CONCLUSIONS: Given the preferences of veterinary clinicians and veterinary students for a template format, clinical pathologists should consider modifying the way they report evaluations of cytologic specimens. Template formats should help standardize reporting of cytologic specimens, thereby improving communication between clinical pathologists and clinicians. However, both clinicians and clinical pathologists need to better understand the purpose of terminology expressing degrees of confidence in such reports.

Background and Objectives: Elderly people may have difficulties understanding the quality and quantity of information about the COVID-19 epidemic, which can put an additional mental strain on their health and well-being. The purpose of this study was to explore the processing of COVID-19 information among older people. Materials and Methods: A qualitative study was carried out in summer 2021. The sampling was based on the snowball method. This approach allowed us to communicate with the next potential participants relatively freely and without reservations. Two female researchers (both MD, PhD) conducted the interviews. All interviews were held in Serbian. The data were analyzed using qualitative content analysis. Results: The interviews were conducted with 13 participants (average age 71 years). The analysis of qualitative content suggested that four topics could be identified: (1) sources of information, (2) information interest and need, (3) reporting of information and (4) suggestions for better reporting. The participants were troubled by the excess of information, repetitive information about death tolls, unqualified people in media discussing the pandemic and inconsistent reporting. These features caused the participants to feel the psychological burden in processing all the pieces of information. Conclusions: The elderly people in Serbia followed mainstream media to get information about COVID-19; however, they perceived a variety of problems with reporting, which made the understanding of the information difficult and psychologically burdensome. These findings should be taken into consideration when delivering health-related information to elderly people.

Mechanisms for reporting child maltreatment (CM) were affected by changes in service provision immediately following the onset of the COVID-19 pandemic.
To examine changes in counts and CPS reporting of CM medical encounters before and after the onset of COVID-19.
All emergency department and inpatient medical encounters with at least one CM diagnosis during the study period at Rady Children\'s Hospital San Diego, the largest pediatric hospital in California between 2016 and November 2021.
Using linked medical record and CPS administrative data, interrupted time series models tested for changes in monthly counts and percentages of CM medical encounters reported to CPS with the onset of COVID-19. Logistic regression tested for the likelihood of a CPS report being associated with a CM encounter.
CM medical encounters totaled 2528, including 793 after the onset of COVID-19. Interrupted time series models indicated with the onset of the pandemic, the counts of CM encounters increased 18 % (RR: 1.18, 95 % CI 1.03-1.34) and the percentages reported to CPS increased 10 % (RR: 1.10, 95 % CI: 1.05-1.17). CM encounters that occurred after the onset of the COVID-19 pandemic had increased odds of a CPS report (fully adjusted model: OR: 1.08; 95 % CI: 1.05-1.12).
This study found increases in monthly counts and a higher percentage of CM medical encounters with CPS reports after the pandemic onset.

In recent years, the corporate social responsibility (CSR) report has gained strategic importance within organizations, especially after the development of strategies and different international regulations, such as the Global Compact and the United Nations\' 2030 Agenda or Directive 2014/95/EU. This, together with the boom in CSR research and business management, has given rise to heterogeneity in the conceptualization and management of CSR report. Consequently, this research study proposes a bibliometric and systematic analysis of 4966 research articles, available in the Scopus repository and published during the period of 2001-2021, to improve the conceptualization and management of CSR communication. The documents analyzed were research articles that included concepts such as CSR, communication, and reporting in the title, abstract and keywords and that were published in the 21st century. The results show an exponential growth in scientific production in recent years, coinciding with the development of strategies and international regulations, and with a high percentage of authors, institutions and countries coming from the European Union. Additionally, the main characteristics of the research are obtained alongside data on the most productive authors, institutions, journals, and countries, in addition to information about their international cooperation networks. Finally, the results present the most relevant contributions in regard to the four research topics detected, namely: (a) accountability; (b) disclosure of performance; (c) management of the organization; (d) corporate strategy; and (e) corporate reputation. These results indicate a broad multidisciplinarity of this line of research. Finally, future lines of research that can contribute to improving or expanding the current findings in this research area are established.

Endoscopy is a commonly performed out patient procedure in the field of Otorhinolaryngology. One of the important parts of an endoscopy is the task of reporting and printing the report to make it available for the patient as well as the treating physician. Most endoscopy reporting software available commercially are expensive and needs appropriate hardware, expertise and manpower for running the same. The following article describes an innovative, time and cost-effective technique, which can be used for reporting purposes using a simple smart phone and a basic data pack with no additional manpower.

BACKGROUND: Natural language processing (NLP) is thought to be a promising solution to extract and store concepts from free text in a structured manner for data mining purposes. This is also true for radiology reports, which still consist mostly of free text. Accurate and complete reports are very important for clinical decision support, for instance, in oncological staging. As such, NLP can be a tool to structure the content of the radiology report, thereby increasing the report\'s value.
OBJECTIVE: This study describes the implementation and validation of an N-stage classifier for pulmonary oncology. It is based on free-text radiological chest computed tomography reports according to the tumor, node, and metastasis (TNM) classification, which has been added to the already existing T-stage classifier to create a combined TN-stage classifier.
METHODS: SpaCy, PyContextNLP, and regular expressions were used for proper information extraction, after additional rules were set to accurately extract N-stage.
RESULTS: The overall TN-stage classifier accuracy scores were 0.84 and 0.85, respectively, for the training (N=95) and validation (N=97) sets. This is comparable to the outcomes of the T-stage classifier (0.87-0.92).
CONCLUSIONS: This study shows that NLP has potential in classifying pulmonary oncology from free-text radiological reports according to the TNM classification system as both the T- and N-stages can be extracted with high accuracy.