Categories Of Information Research Articles

Legal Review of the Implementation of Public Information Openness Based on Laws and Internal Regulations at PT Pelabuhan Indonesia (Persero) Regional 1

Public information disclosure is a key factor in building public trust and establishing good governance to ensure transparent and accountable management. This study aims to analyze the implementation of public information disclosure at PT Pelindo Regional 1 in accordance with Law No. 14 of 2008 on Public Information Disclosure, applicable board regulations, and established public information service guidelines. This research employs a normative legal method, focusing on analyzing relevant legislation, complemented by an empirical approach to collect and examine data related to the implementation of public information disclosure at PT Pelindo Regional 1. Data collection techniques include interviews with relevant stakeholders within the company to gain insights into the practical implementation of public information disclosure. The findings reveal that although PT Pelindo Regional 1 has made efforts to fulfill its public information disclosure obligations, challenges remain in delivering information to the public, particularly in terms of public awareness regarding their rights to access information. The study also identifies the categories of information disclosed to the public and information exempted from public disclosure, highlighting the limitations imposed on public access to certain information. Based on these findings, the study provides recommendations to enhance the effectiveness of public information disclosure at PT Pelindo, including increasing public awareness campaigns and strengthening existing public information systems.

An OWL-S based specification model of dynamic entity services for IoT

Purpose One of the open questions is how to define a complete semantic description of an Internet of Things (IoT) system and how to ensure the device’s identification in this type of system. To answer this question, the authors needed to propose a mechanism to describe an IoT system based on an IoT context ontology description. This mechanism is based on semantic web services creation to identify objects in IoT system. This paper aims to propose a model to describe the IoT system. The authors suggest an approach to describe each category of contextual information separately and ensure the adaptation in the IoT system. Design/methodology/approach One of the open questions is how to define a complete semantic description of an IoT system and how to ensure the device’s identification in this type of system. To answer this question, the authors needed to propose a mechanism to describe an IoT system based on an IoT context ontology description. This mechanism is based on semantic web services creation to identify objects in IoT system. Findings One of the open questions is how to define a complete semantic description of an IoT system and how to ensure the device’s identification in this type of system. To answer this question, the authors needed to propose a mechanism to describe an IoT system based on an IoT context ontology description. This mechanism is based on semantic web services creation to identify objects in IoT system. This paper aims to propose a model to describe the IoT system. The authors suggest an approach to describe each category of contextual information separately and ensure the adaptation in the IoT system. Originality/value One of the open questions is how to define a complete semantic description of an IoT system and how to ensure the device’s identification in this type of system. To answer this question, the authors needed to propose a mechanism to describe an IoT system based on an IoT context ontology description. This mechanism is based on semantic web services creation to identify objects in IoT system. This paper aims to propose a model to describe the IoT system. The authors suggest an approach to describe each category of contextual information separately and ensure the adaptation in the IoT system.

Primary School Students' Perceptions of Artificial Intelligence: Metaphor and Drawing Analysis

ABSTRACTDue to the frequent use of artificial intelligence (AI) technologies in daily life, it is thought that primary school students acquire information about this concept from various sources. The way these sources present AI may affect students' perceptions of AI. In the study, it was aimed to examine the perceptions of third and fourth grade primary school students about AI through metaphors and drawings. This research, which was conducted with the participation of 262 students, was conducted with the phenomenological design. When the metaphors of the participants were analysed, it was determined that they produced 100 metaphors, and these metaphors were evaluated in 17 categories as humanistic feature, information source, danger, development, superhuman feature, service, source of happiness, productivity, orientation, commitment, pervasiveness, necessity, security, speed, difficulty, virtual environment and uncertainty. Accordingly, it was determined that the participants evaluated AI from many different perspectives and produced the most metaphors in the categories of humanistic feature, information source and danger. It was determined that the metaphors human, brain and living were prominent in the human characteristic category; the metaphors teacher, wise and book were prominent in the source of information category; and finally, the metaphors enemy, weapon and monster were prominent in the danger category. When the drawing findings were analysed, it was determined that 37 codes represented four categories: purpose, object, interaction and environment. In the purpose category, service, source of information, and source of happiness; in the object category, mostly humanoid robot; in the interaction category, emphasising interaction; and in the environment category, the environment was not specified. In line with the findings obtained, literature discussions were made and suggestions were made.

Scientometric tools in strategising the development of scientific periodicals: the case of the journal Digital Models and Solutions

The present study is dedicated to the development of scientometric tools for the strategic advancement of a scientific periodical, exemplified by the journal of the Ural State University of Economics, “Digital Models and Solutions”. The research involved a compre hensive analysis of the publication activity and thematic profile of this journal, as well as a general statistical examination of Russian academic journals indexed in the Russian Science Citation Index (RSCI) that pertain to the upper echelon of the State Rubricator of Scientific and Technical Information category “Informatics”. Primary data collection was conducted using the platforms elibrary.ru, CyberLeninka and CrossRef. The data analysis and visuali zation were performed utilizing the R programming language. The construction of thematic networks was carried out using the VOSviewer software, while additional visualization of certain results was executed using Scimago Graphica. The findings of the study identify the current positioning of the journal “Digital Models and Solutions” among scientific publi cations in informatics, define target scientometric indicators for its prospective growth, and propose specific recommendations for the strategic development of the journal, aimed at enhancing its competitiveness and scholarly impact.

Comparative analysis of regression algorithms for drug response prediction using GDSC dataset

BackgroundDrug response prediction can infer the relationship between an individual’s genetic profile and a drug, which can be used to determine the choice of treatment for an individual patient. Prediction of drug response is recently being performed using machine learning technology. However, high-throughput sequencing data produces thousands of features per patient. In addition, it is difficult for researchers to know which algorithm is appropriate for prediction as various regression and feature selection algorithms exist.MethodsWe compared and evaluated the performance of 13 representative regression algorithms using Genomics of Drug Sensitivity in Cancer (GDSC) dataset. Three analyses was conducted to show the effect of feature selection methods, multiomics information, and drug categories on drug response prediction.ResultsIn the experiments, Support Vector Regression algorithm and gene features selected with LINC L1000 dataset showed the best performance in terms of accuracy and execution time. However, integration of mutation and copy number variation information did not contribute to the prediction. Among the drug groups, responses of drugs related with hormone-related pathway were predicted with relatively high accuracy.ConclusionThis study can help bioinformatics researchers design data processing steps and select algorithms for drug response prediction, and develop a new drug response prediction model based on the GDSC or other high-throughput sequencing datasets.

Experience of Implementing Assistive Technologies in Ukrainian and Foreign Document and Information Structures

Objective. The article examines the global experience of digital inclusion and the state of the creation of information spaces for people with special needs in leading document and information institutions of society. Methods. The research was conducted based on content analysis of 20 sites of US academic libraries and webometric analysis of 41 sites of Ukrainian document and information structures (libraries, archives, and museums) using the "Web Accessibility Checker" functionality. Monitoring of library sites was carried out during May 2024. Results. The conducted analysis proved that today digital inclusion in document and information institutions of libraries in the USA, Great Britain, and France is aimed at overcoming the lack of information resources for different groups of users, introducing computers with special software and assistive technologies for people with disabilities, and creating comfortable information space. This activity is only a desirable prospect for Ukraine's libraries, although work in this direction is unfolding. Conclusions. A conclusion was made regarding the importance of implementing assistive technologies in document and information structures and ensuring comfortable access to information for various categories of users, including those with special educational needs and people with disabilities. The development of digital inclusion and the creation of specialized products and services for special users is a strategic direction of the development of document and information institutions. Libraries are leaders in implementing and using assistive technologies among various document and information institutions. Compared to museums and archives, they have the greatest experience in serving users with disabilities and the most powerful potential for developing social inclusion and embodying diversity in the digital environment.

A Scene Knowledge Integrating Network for Transmission Line Multi-Fitting Detection.

Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is proposed. Firstly, the particularity of the scene in the multi-fitting detection task is analyzed. Hence, the aggregation of the fittings is defined as the scene according to the professional knowledge of the power field and the habit of the operators in identifying the fittings. So, the scene knowledge will include global context information, fitting fine-grained visual information and scene structure information. Then, a scene filter module is designed to learn the global context information and fitting fine-grained visual information, and a scene structure module is designed to learn the scene structure information. Finally, the scene semantic features are used as the carrier to integrate three categories of information into the relative scene features, which can assist in the recognition of the occluded fittings and the tiny-scale fittings after feature mining and feature integration. The experiments show that the proposed network can effectively improve the performance of the multi-fitting detection task compared with the Faster R-CNN and other state-of-the-art models. In particular, the detection performances of the occluded and tiny-scale fittings are significantly improved.

Named entity recognition for de-identifying Spanish electronic health records.

There is an increasing and renewed interest in Electronic Health Records (EHRs) as a substantial information source for clinical decision making. Consequently, automatic de-identification of EHRs is an indispensable task, since their dissociation from personal data is a necessary prerequisite for their dissemination. Nevertheless, the bulk of prior research in this domain has been conducted using English EHRs, given the limited availability of annotated corpora in other languages, including Spanish. In this study, the automatic de-identification of medical documents in Spanish was explored. A private corpus comprising 599 genuine clinical cases was annotated with eight different categories of protected health information. The prediction problem was approached as a named entity recognition task and two deep learning-based methodologies were developed. The first strategy was based on recurrent neural networks (RNN) and the second, an end-to-end approach, was based on Transformers. In addition, we have implemented a procedure to expand the amount of texts employed for model training. Our findings demonstrate that Transformers surpass RNNs in the de-identification of clinical data in Spanish. Particularly noteworthy is the excellent performance of the XLM-RoBERTa large Transformer, achieving a rigorous strict-match micro-average of 0.946 for precision, 0.954 for recall, and an F1 score of 0.95 when applied to the amplified version of the corpus. Furthermore, a web-based application has been created to assist specialized clinicians in de-identifying EHRs through the aid of the implemented models. The study's conclusions showcase the practical applicability of the state-of-the-art Transformers models for precise de-identification of clinical notes in real-world medical settings in Spanish, with the potential to improve performance if continual pre-training strategies are implemented.

Local context review by single institutional review boards: Results from a modified Delphi process.

Local context is the most common concern regarding use of a single institutional review board (sIRB). Yet what "local context" constitutes remains underspecified. Developing a shared understanding of the goals of local context review, the categories of information that should be considered, as well as the types of studies for which sIRB review may be inappropriate, are critical for ensuring that sIRB review provides adequate protections for human subjects. We conducted a three-round modified Delphi process convening individuals with expertise in the conduct and oversight of multisite research. Delphi surveys explored: (1) the goals of local context review; (2) the types of information that should be considered; and (3) study types that should be exempted from sIRB requirements. Twenty-one experts participated. Experts agreed that (1) local context review should aim to both protect local participants and ensure compliance and (2) that four types of information should be considered (population/participant-level characteristics; investigator and research team characteristics; institution-level characteristics; and state and local laws). There was less consensus about whether existing processes facilitated adequate consideration of this information. Experts agreed that exemptions from sIRB requirements should be permitted but disagreed about when and in what circumstances. There is overlapping consensus about both the goals of local context review and the types of information that should be assessed. Future work remains, however, to develop effective processes to best realize the goals of local context review - and do so with appropriate efficiency.

Controversies in implementing the exemption policy for the elderly healthcare services in Tanzania: experiences from the priority setting process in two selected districts

BackgroundHealth financing system in Tanzania changed in the early 1990s as a result of the introduction of cost-sharing policies by the 1990s health sector reforms. The reforms brought about user fees which has led to inequity in access to health care services and catastrophic health expenditure among the elderly. In efforts to reduce the inequity gap among vulnerable groups, in 1994, the government introduced an exemption and waiver policy. More than three decades later, inequity in health care services access has persisted with the elderly population being more affected. The latter poses questions on the implementation efficiency of the exemption policy. We aimed to assess the implementation of the exemption policy on access to health services among the elderly in Tanzania by learning from the experiences of the priority setting process in two districts of western Tanzania.MethodsAn exploratory qualitative case study adopting Key informant interviews (KIIs) was used to collect data in Nzega and Igunga districts. The key informants involved the representatives of the planning team and decision makers from the community, health facility and district level. Information saturation was attained after the 24th interview and thus data collection ended. The content analysis approach was used to analyse the data.FindingsAlthough there is a designated office that deals with exemptions for the elderly in health facilities, there are challenges in accessing health services. The challenges include insufficient drugs, some laboratory tests not covered by the exemption, a lengthy process to access service, poor financial mechanisms for exempted services, inadequate information and clarity of the exemption categories, and limited to no involvement of different stakeholders in the exemption process.ConclusionThe exemption policy was introduced to help disadvantaged groups, including the elderly. However, its implementation encountered challenges which burden both the elderly and the health facilities. Its implementation has thus become a controversy to its initial aim that was to relieve the elderly from high healthcare costs. Revisiting the policy through a thorough stakeholders’ engagement and establishing alternative financing of the exemption policy are recommended.

Herd Behavior and Harm: How Negative Online Reviews Affect Independent Music Venues

Abstract The influence of Yelp reviews on independent music venues is acknowledged, though the exact nature of this impact remains unclear. As negative reviews increase, they can shape public perception, leading even those with neutral or positive experiences to avoid the venue. This behavior, known as the bandwagon effect or herd behavior, reinforces the venue’s negative reputation and further influences potential patrons. The study examines 1,423 independent venues and their Yelp reviews, dating back to the venues’ opening or as far as 2004, the year Yelp was founded. Subsequently, the study leverages a meticulous extraction methodology involving 30,486 keywords derived from an extensive dataset of 2,305,734 mostly negative or neutral Yelp reviews that were rated 1, 2, or 3 out of 5 stars. These keywords have been systematically categorized into seven codified groups: (1) Noise/Music, (2) Food/Drink, (3) Service, (4) Place/Atmosphere, (5) Transactions, (6) Positive words, and (7) Negative words. Moreover, a devised sentence extraction mechanism has been coded and implemented to capture contextually relevant information for each distinct word category. Subsequently, a sentiment analysis was performed on the extracted sentences to evaluate the overall impressions associated with music/noise-related topics. The significance of this study is underscored by its potential to augment our comprehension of the impact of Yelp reviews on independent music venues, offering actionable insights for venue management, and contributing substantively to the broader discourse surrounding the intersection of online reviews, big data, live music, noise studies, and hospitality.

Enhancing strategic decision-making in built asset management through BIM-Enabled asset information modelling (AIM) for public buildings in Ethiopia: A fuzzy-AHP analysis

BIM-Enabled Asset Information Modelling (AIM) entails incorporating extensive data, known as big data, into digital platforms for informed decision-making. However, the lack of accurate and reliable data and the immaturity of BIM integration in existing buildings lead to operational phase performance inefficiencies due to inadequate data access. A strategic approach using BIM-enabled AIM is proposed to address these challenges, with the goal of enhancing data accessibility and adequacy for the operational team's performance. This study aims to develop a framework of information requirements that supports operational strategic decisions in asset portfolio management. To develop the framework, we employed a methodology that combines the Analytic Hierarchy Process (AHP) a structured technique for organizing and analysing complex decisions with fuzzy logic, which helps handle uncertainty in experts' judgments. A comprehensive questionnaire based on the Analytic Hierarchy Process (AHP) methodology was developed to gather expert insights on prioritizing information requirements, and it was administered to 11 experts selected for their diverse expertise in problem area. Cost, risk, and business value as selection criteria, while technical, managerial, financial, legal, and commercial categories of information are considered as alternatives in the AHP hierarchy. Utilizing the described methodology, fuzzy-AHP analysis revealed distinct variations in information requirements across the strategic decisions of maintain/keep, improve/adapt, and deconstruct/disassemble. For decisions on whether to maintain/keep buildings, the primary information requirement is managerial (39.6 %), followed by legal (20.7 %) and commercial (20 %), guiding strategic decisions. In contrast, improve/adapt decisions prioritize technical information (39 %), with financial (15.5 %) and legal (13.5 %) considerations also being significant. For the deconstruct/disassemble decisions, technical information requirements are most critical (55.5 %), followed by legal (16.6 %) and commercial (12.8 %) information. The findings highlight the need for tailored data generation strategies in existing buildings to address specific decision requirements, aiding in planning and resource allocation towards efficient AIM. The primary limitation of this study is its reliance on a small pool of 11 experts, which may limit the generalizability of the findings. Future research should aim to broaden the expert base to enhance the applicability and robustness of the results.

A combined support vector regression with a firefly algorithm for prediction of energy consumption in wastewater treatment plants.

Wastewater treatment plants (WWTPs) comprise energy-intensive processes, serving as primary contributors to overall WWTP costs. This research study proposes a novel approach that integrates support vector regression (SVR) with the firefly algorithm (FFA) for the prediction of energy consumption in a WWTP in Chlef City, Algeria. The database comprises a comprehensive set of 1,653 samples, capturing diverse information categories. It includes chemical and physical characteristics, encompassing chemical oxygen demand, 5-day biochemical oxygen demand, potential of hydrogen, water temperature, total suspended sediment in water and basin, influent N-NH3 concentration, number of aerators, and operating time. Additionally, the hydraulic and energy-related parameters are represented by the flow entered at the station and the energy consumed by aerators, respectively. Finally, meteorological data, comprising rainfall, temperature, relative humidity, and the aridity index, are part of the dataset required for analysis. In this regard, 15 different models that correspond to 15 different combinations of input parameters are assessed in this study. The results show that the SVR-FFA-15 can render an improvement in the prediction accuracy of energy consumption in WWTPs. This study provides a useful tool for managing the energy consumption of wastewater treatment and makes insightful recommendations for future energy savings.

Token Embeddings Augmentation benefits Parameter-Efficient Fine-Tuning under long-tailed distribution

Pre-trained vision-language models, particularly those utilizing CLIP, have advanced various visual tasks. Parameter-Efficient Fine-Tuning (PEFT) on such models is a mainstream trend for downstream tasks. Despite advancements, long-tailed distribution still hampers image recognition performance in current PEFT schemes. Therefore, this paper proposes Token Embeddings Augmentation (TEA) to tackle long-tailed learning under PEFT paradigm. Based on patch token semantic mining, TEA uncovers category-specific semantic details within patch tokens to enhance token embeddings, named Patch-based Embeddings Augmentation (PEA). Then, a Probability Gate (PG) strategy is designed to effectively enrich semantic information of tail categories using enhanced embeddings. A Token Embeddings Consistency (TEC) loss is further introduced to prioritize category semantic information within tokens. Extensive experiments on multiple long-tailed distribution datasets show that our method improves the performance of various PEFT methods with different classification loss functions, especially for tail categories. Our optimal approach achieves the state-of-the-art results on multiple datasets with negligible parameters or inference latency, thus enhancing the practicality of PEFT in long-tailed distributions.

Exploring challenges and demands of practising healthy eating in young adults: a qualitative study

Transitioning from adolescence to adulthood for individuals aged 18 to 29 involves challenges and demands. This research explores the key challenges and needs of the lived experiences of young adults aged 18 to 29 in practising healthy eating. A qualitative research methodology was employed, using a phenomenological approach to investigate personal and subjective experiences. The study revealed five focused topics and 20 themes clustered into thirteen information categories reflecting their challenges and demands. It highlights knowledge of healthy eating practices among young adults and the need for targeted support systems and interventions to assist young adults in managing these challenges.

The African cigarette price data landscape: an overview of gaps and opportunities

IntroductionAfrica has the lowest cigarette taxes of any region. Price data are needed to produce evidence that informs tax policy change.ObjectiveTo locate and describe datasets that provide information on cigarette...

BEYOND – Building an Experience to Yield Optimal Nautical Displays – Measuring Nautical Situation Awareness

Abstract The growing digitisation and integration of ship operations, navigation, communication, safety equipment and systems have expanded the functional scope of modern navigation systems. However, this has also increased the workload for the crew, making it more challenging to cultivate situation awareness and make appropriate decisions, particularly in critical situations. The research project, BEYOND – Building an Experience to Yield Optimal Nautical Displays, aims to develop and validate a test methodology for evaluating maritime Human-Machine Interfaces (HMI). Besides system usability and user experience, maritime HMIs are generally evaluated in terms of the situation awareness acquired during navigation. For this purpose, the established Situation Awareness Global Assessment Technique (SAGAT) was adapted such that participants experienced a series of simulated navigation scenarios and were interrupted at unexpected intervals to answer situation awareness questions. This paper addresses the challenge of formulating the appropriate situation awareness questions and their associated frameworks. An assembly of questions reflecting the knowledge of a navigator was created, taking into account different categories of information, including the own ship, the route taken, and the surrounding area. The number of questions needed to capture the necessary knowledge comprehensively was also defined. Additionally, it was crucial to determine the appropriate answer options for each question. Besides graphically presented multiple-choice questions, where the answer had to be selected based on a screenshot of the environment, there are also alphanumeric questions where the participant had to retrieve knowledge from the situation and enter numerical values. For instance, this included information about the course, navigation hazards, etc. A major challenge in assessing these numerical values was determining the acceptable range, i.e., the thresholds that define an answer as correct or incorrect. This paper outlines the BEYOND methodology developed for the comprehensive testing of maritime HMIs, focusing primarily on situation awareness assessment. It describes the steps taken to compile a list of situation awareness questions and their corresponding accuracy thresholds based on the findings from expert interviews. The paper also discusses the experimental results of a user study conducted to test the overall methodology.

Discriminative Deep Canonical Correlation Analysis for Multi-View Data.

Over the past few years, multimodal data analysis has emerged as an inevitable method for identifying sample categories. In the multi-view data classification problem, it is expected that the joint representation should include the supervised information of sample categories so that the similarity in the latent space implies the similarity in the corresponding concepts. Since each view has different statistical properties, the joint representation should be able to encapsulate the underlying nonlinear data distribution of the given observations. Another important aspect is the coherent knowledge of the multiple views. It is required that the learning objective of the multi-view model efficiently captures the nonlinear correlated structures across different modalities. In this context, this article introduces a novel architecture, termed discriminative deep canonical correlation analysis (D2CCA), for classifying given observations into multiple categories. The learning objective of the proposed architecture includes the merits of generative models to identify the underlying probability distribution of the given observations. In order to improve the discriminative ability of the proposed architecture, the supervised information is incorporated into the learning objective of the proposed model. It also enables the architecture to serve as both a feature extractor as well as a classifier. The theory of CCA is integrated with the objective function so that the joint representation of the multi-view data is learned from maximally correlated subspaces. The proposed framework is consolidated with corresponding convergence analysis. The efficacy of the proposed architecture is studied on different domains of applications, namely, object recognition, document classification, multilingual categorization, face recognition, and cancer subtype identification with reference to several state-of-the-art methods.

AI hallucination: towards a comprehensive classification of distorted information in artificial intelligence-generated content

Amidst the burgeoning information age, the rapid development of artificial intelligence-generated content (AIGC) has brought forth challenges regarding information authenticity. The proliferation of distorted information significantly impacts users negatively. This study aims to systematically categorize distorted information within AIGC, delve into its internal characteristics, and provide theoretical guidance for its management. Utilizing ChatGPT as a case study, we conducted empirical content analysis on 243 instances of distorted information collected, comprising both questions and answers. Three coders meticulously interpreted each instance of distorted information, encoding error points based on a predefined coding scheme and categorizing them according to error type. Our objective was to refine and validate the distorted information category list derived from the review through multiple rounds of pre-coding and test coding, thereby yielding a comprehensive and clearly delineated category list of distorted information in AIGC. The findings identified 8 first-level error types: “Overfitting”; “Logic errors”; “Reasoning errors”; “Mathematical errors”; “Unfounded fabrication”; “Factual errors”; “Text output errors”; and “Other errors”, further subdivided into 31 second-level error types. This classification list not only lays a solid foundation for studying risks associated with AIGC but also holds significant practical implications for helping users identify distorted information and enabling developers to enhance the quality of AI-generated tools.

What Is Culture Made of? An Exploratory Study of Ethical Cultures and Provider Perspectives on the Care of Periviable Neonates

Objective Studies examining intercenter variation in neonatal intensive care unit practices at the limits of fetal viability have hypothesized that institutional “culture” can be one of many factors that impact patient care. This study aimed to describe institutional culture at a single, large academic center with regard to the antenatal consultation, resuscitation, and postnatal management of periviable neonates. Study Design Members of six clinical groups—attending and fellow maternal–fetal medicine physicians, attending and fellow neonatal–perinatal medicine physicians, neonatal nurses, advanced practiced neonatal nurses, pediatric hospitalist physicians, and neonatal respiratory therapists—were invited to complete qualitative, semi-structured interviews. All audio recordings were transcribed. Dedoose software was used to complete team-based coding and thematic analysis. Results Twenty-two interviews were completed. Thematic analysis revealed three central themes described by participants as contributory to institutional culture: Perception, referring to factors based on individual attitudes and insights, Statements of Information, referring to factors anchored in more objective concepts such as outcomes data and institutional policy, and Dynamic Factors, referring to the relatively fluid factors of institutional culture that interact with both Perception and Statements of Information. Participants were more likely to mention factors in the Perception category (n = 430) compared with factors in the Information category (n = 225), and although the latter were described as critical components of antenatal counseling and perinatal management, the philosophy of our unit appeared to be more heavily rooted in institutional memory and individual belief systems. Conclusion Our data demonstrate a personal undertone to institutional culture at the limits of viability, with an emphasis on individual attitudes and subjective interpretations of fact rather than empirical data. As the landscape of neonatology continues to change, understanding those factors that contribute to culture remains a necessary step toward deconstructing institutional belief systems and optimizing clinical care. Key Points