Tool For Interpretation Research Articles

Leveraging enhanced egret swarm optimization algorithm and artificial intelligence-driven prompt strategies for portfolio selection

In the financial field, constructing efficient investment portfolios is a focal point of research, encompassing asset selection and optimization of asset allocation. With the advancements in Large Language Models (LLMs), generative Artificial Intelligence (AI) tools have showcased capabilities never seen before. However, the black-box nature of these tools renders their outputs difficult to interpret directly, often necessitating iterative fine-tuning to align with users’ expected outcomes. This study presents a structured prompt framework specifically designed for stock selection, aiming to provide direct and interpretable stock-selecting tools for investors of various levels. By creating representative scenarios and combining them into different cases for experimentation, we can explore how the construction of prompts influences the responses generated by generative AI tools. Additionally, this paper proposes a novel algorithm that combines the Nonlinear-Activated Beetle Antennae Search strategy with the Egret Swarm Optimization Algorithm (NBESOA) to address the Mean-Variance Portfolio Selection problem with Transaction Costs and Cardinality Constraints (MVPS-TCCC), utilizing real stock market data to construct portfolios based on generative AI tools recommendations. Simulation results indicate that, compared to other algorithms, NBESOA prefers optimizing portfolio configurations to achieve the highest Sharpe Ratio with the strictest constraints, bringing the outcomes closer to the portfolio’s efficient frontier.

Inhibitory Effect of Aqueous Extract of Sacoglottis gabonensis on Weight Gain in Swiss Mice Exposed to Aspartame

Aim: To evaluate the Inhibitory effect of aqueous extracts of Sacoglottis gabonensis on weight gain in Swiss mice exposed to aspartame. Study Design: The study was a completely randomized design employing relevant statistical tools for analysis and interpretation. Place and Duration of Study: The study was carried out in the Department of Animal and Environmental Biology, Rivers State University. The experiment lasted for 90 days between April and June 2022. Methodology: A total of 30 adult male Swiss mice (mean weight 23.07±0.12g) were divided into six groups of five animals each. Group A was the negative control which received neither Sacoglottis gabonensis nor Aspartame, group B was the positive control which received 50mg/kg/bw/day of Aspartame only, group C received 50mg/kg/bw/day of Aspartame with 250mg/kg/bw/day of aqueous bark extract of S. gabonensis, group D received 50mg/kg/bw/day of Aspartame with 250mg/kg/bw/day of aqueous leaf extract of S. gabonensis, while groups E and F received 50mg/kg/bw/day of Aspartame with a combination of 250mg/kg/bw/day and 500mg/kg/bw/day of extracts of S. gabonensis bark and leaf extracts respectively. The experimental animals were weighed twice weekly for 30, 60 and 90 days duration and recorded to the nearest 0.01g. Mean weight from each duration was taken and subjected to statistical analysis using Statistical Analyses System SAS 9.4 Results: There was significant increase (p<0.05) in weight in the 30, 60 and 90-days exposure periods in group B. The groups that received the combination of aspartame and S. gabonensis particularly at higher doses showed a significant decrease in weight across the exposure periods when compared to the group that received aspartame only and the control group. Conclusion: This result suggests that S. gabonensis reduced aspartame-induced weight gain, indicating its potential benefits for weight management. Further research is needed to explore the underlying mechanisms and health implications.

Convolutional sparse coding network for sparse seismic time-frequency representation

Seismic time-frequency (TF) transforms are essential tools in reservoir interpretation and signal processing, particularly for characterizing frequency variations in non-stationary seismic data. Recently, sparse TF transforms, which leverage sparse coding (SC), have gained significant attention in the geosciences due to their ability to achieve high TF resolution. However, the iterative approaches typically employed in sparse TF transforms are computationally intensive, making them impractical for real seismic data analysis. To address this issue, we propose an interpretable convolutional sparse coding (CSC) network to achieve high TF resolution. The proposed model is generated based on the traditional short-time Fourier transform (STFT) transform and a modified UNet, named ULISTANet. In this design, we replace the conventional convolutional layers of the UNet with learnable iterative shrinkage thresholding algorithm (LISTA) blocks, a specialized form of CSC. The LISTA block, which evolves from the traditional iterative shrinkage thresholding algorithm (ISTA), is optimized for extracting sparse features more effectively. Furthermore, we create a synthetic dataset featuring complex frequency-modulated signals to train ULISTANet. Finally, the proposed method's performance is subsequently validated using both synthetic and field data, demonstrating its potential for enhanced seismic data analysis.

Interpretable causal machine learning optimization tool for improving efficiency of internal carbon source-biological denitrification

Interpretable causal machine learning optimization tool for improving efficiency of internal carbon source-biological denitrification

The contribution of environmental interpretation in nature protection

The interpretation of natural values and the management of visitors in protected areas are related in multiple ways and are an important link in nature protection activities. This paper examines the basic elements of the interpretation and management of visitors in the selected protected areas of the Republic of Croatia. Protected areas are one of the main attributes of the tourist image of Croatia, with significant economic effects in the generated traffic of visitors. However, this can also contribute to the degradation of natural values or, on the other hand, can help protect nature. Due to such challenges, it is necessary to better understand the importance of visitor centres, tourist tours and the way in which codes of ethics are applied in protected areas. In this paper the representation of environmental interpretation techniques and tools in seven protected areas in Croatia were investigated, using information that was obtained mainly through survey research. The basic results indicate that Croatia has made progress in the previous period in the field of interpretation of natural and cultural values in its protected areas. Such progress is not only related to regions where tourism has a mass character and where visits are significant, but has also been made in remote continental parts of the country. Such an approach in the development of protected areas also indicates that these areas are not only massive tourist destinations, but also places for a more complex understanding of the concept of nature protection and sustainable development associated with the use of its potentials.

Naturalists’ Technology Use Supports Nature Education

Naturalists act as a link between scientific knowledge and the public’s understanding of natural history and conservation efforts. For them to be successful, they need access to current content and resources. Incorporating tablet technology into naturalists’ endeavors can serve as an interpretation tool that facilitates participation and offers needed support to new/emerging naturalists. Our study, guided by an Attention Participation Framework, used a qualitative approach to explore the naturalists’ use of tablets during a technology-enhanced nature education program. We developed an app to support nature hikes for students led by naturalists-in-training ( n = 72). We investigated how naturalists used tablet-based technology to support nature hike activities and their perspectives on technology as a tool for nature education. We found that naturalists relied on the app for reference throughout the hikes. Additionally, all of the naturalists held a positive perspective toward using tablets as an interpretive tool and recommended them for future use.

Multidisciplinary Optimization of a Mixed-flow Impeller with 140 Parameters and its Shapley Additive Explanations

Abstract In recent years, we have made several improvements to the geometric parameterization method, the surrogate model method, and the sampling method, with the goal of making the traditional surrogate model-based optimization method applicable to aerodynamic optimization of hundreds of parameters with reasonable computational cost. However, increasing the number of control parameters raises two additional issues. First, the impeller geometry becomes too complex to ensure the required mechanical performance. Second, the optimization mechanism becomes difficult to understand with too many parameters. To address the first issue, this paper builds a multidisciplinary optimization platform to achieve the optimization of a large flow coefficient mixed-flow impeller under 140 control parameters, resulting in a significant improvement in both aerodynamic and mechanical performance. To address the latter, a novel machine learning interpretation tool, Shapley Additive Explanations (SHAP), is introduced in this paper. Using this methodology, the contribution of all 140 parameter values in the final optimal impeller to each aspect of the performance improvement is presented in this paper, providing the first in-depth understanding of the intricate mechanisms involved in the multidisciplinary optimization of hundreds of control parameters.

Development and evaluation of a novel framework to enhance k-NN algorithm’s accuracy in data sparsity contexts

This paper presents a novel framework for implementing the k-NN algorithm, designed to enhance its accuracy in contexts with sparse data. The framework addresses limitations in the algorithm’s training process by optimizing data structures. It employs composite datasets generated from the initial data using a data-driven fuzzy Analytic Hierarchy Process weighting scheme. This approach is designed to enhance the informational content in the initial datasets, thus reducing the entropy and implementation uncertainty. The framework was evaluated using 75 publicly available datasets and 3 generated datasets, demonstrating significant accuracy improvements across various k-parameter values. The findings were rigorously generalized using non-parametric hypothesis tests; while the resulting sensitivity was assessed by applying different distance metrics. By enhancing informational content, the composite data structures contribute to both accuracy improvements and scalability, particularly in data-sparse contexts. This relationship underscores the critical role of entropy in enhancing the performance of explainable machine learning algorithms, providing a valuable and interpretable tool for transforming data structures in sparse data environments.

The Enderby Bark Shield: A New Model for the Ancient World

A 2300 year old bark shield found in Enderby, Leicestershire, in 2015 is the only known example of its type. Made from the bark of a willow tree, it has a woven basket boss, a roundwood handle, and a rim of split roundwood edging and lime bast bindings. Pre-Roman shields made from organic materials rarely survive in Britain and Ireland and those without metal components are exceptionally rare. Contemporaneous wooden shields are known from anaerobic environments in Scandinavia but, unlike Enderby, none of these has a body of tree bark. The complexity of the design of the Enderby shield, the skill with which it was made, and the similarities between this and metal examples suggests it was a tried and tested design, rather than a one-off. With no other example against which to compare it, experiments in reproducing the shield have been used as a tool for interpretation and have proved vital to understanding the original design. As a result of this research, it is proposed that this single artefact represents a more commonly available form of shield in the 1st millennium bc than does any metal enhanced version.

The Framework of Modeling Buddhist Head in 3D with Semantic Information

Abstract. Buddhist statues hold profound spiritual and cultural significance, yet their comprehensive analysis and identification remain largely reliant on manual annotation and expert scrutiny. Despite advancements in 3D modeling, the nuanced representation of these statues, essential for automated analysis, remains elusive. Integrating semantic information such as symbolic and cultural elements poses a further challenge. There currently exists no standardized method for modeling Buddhist heads in 3D with semantic information. This paper proposes a framework for the component-based analysis of Buddhist statues, particularly focusing on their heads, to bridge this gap. Drawing from diverse disciplines including archaeology, art history, and conservation, the framework leverages advancements in 3D reconstruction to provide a detailed depiction of intricate features. Building upon existing research outcomes, the framework facilitates the seamless integration of semantic information, capturing not only the physical attributes but also the cultural, historical, and spiritual dimensions of these sculptures. This approach aids in overcoming challenges in automated analysis and virtual restoration, providing a dynamic tool for exploration, preservation, and interpretation of cultural heritage. By enabling interdisciplinary synergy, the framework emerges as a dynamic tool for exploring, preserving, and interpreting the profound heritage of Buddhist art with unprecedented depth and clarity.

Comparative Analysis of Machine Learning Models for Predicting Student Success in Online Programming Courses: A Study Based on LMS Data and External Factors

Early prediction of student performance in online programming courses is essential for implementing timely interventions to enhance academic outcomes. This study aimed to predict academic success by comparing four machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Network (Multilayer Perceptron, MLP). We analyzed data from the Moodle Learning Management System (LMS) and external factors of 591 students enrolled in online object-oriented programming courses at the Universidad Estatal de Milagro (UNEMI) between 2022 and 2023. The data were preprocessed to address class imbalance using the synthetic minority oversampling technique (SMOTE), and relevant features were selected based on Random Forest importance rankings. The models were trained and optimized using Grid Search with cross-validation. Logistic Regression achieved the highest Area Under the Receiver Operating Characteristic Curve (AUC-ROC) on the test set (0.9354), indicating strong generalization capability. SVM and Neural Network models performed adequately but were slightly outperformed by the simpler models. These findings suggest that integrating LMS data with external factors enhances early prediction of student success. Logistic Regression is a practical and interpretable tool for educational institutions to identify at-risk students, and to implement personalized interventions.

Effects of poultry by-product meal and complete replacement of fish oil with alternative oils on growth performance and gut health of rainbow trout (Oncorhynchus mykiss): a FEEDNETICS™ validation study

BackgroundAquaculture, traditionally a form of biotechnology, has evolved to integrate innovative biotechnological applications, such as advanced feed formulations, aimed at improving the growth performance and health of farmed fish species. In the present study, the effects of feeding rainbow trout with novel feed formulations were investigated. Fish growth, gut and liver morphology, the concentration of fatty acids in the fillet, and volatile fatty acids in the gut were assessed. The study also validated scenarios from in vivo experiments using a nutrient-based model called FEEDNETICS™. This globally used model serves as a tool for data interpretation and decision support in the context of precision fish farming.MethodsAlternative protein and oil sources, including poultry by-product meal (PBM) and natural algae oil, were explored as sustainable replacements for fishmeal (FM) and fish oil (FO). A 90-day feeding trial was conducted using rainbow trout, comparing two isoproteic, isolipidic and isoenergetic diets. The control diet contained 15% FM, 5% PBM, and 8% FO, while the test diet replaced FM with 15% PBM and 5% feather meal hydrolysate (FMH), and fully substituted FO with VeraMaris® natural algae oil and rapeseed oil.ResultsPBM successfully replaced FM protein without negatively affecting feed intake, growth performance or feed utilization in trout. The combination of PBM and natural algae oil was well tolerated by the trout and showed no negative effects on gut health. A detailed analysis of fatty acids in the fillet revealed that PUFAs of the n3 and n6 series were significantly higher in the PBM group than in the FM group. Values of fatty acid-related health indexes, including atherogenicity index, and thrombogenicity index, confirmed the high nutritional value of trout filet, thus representing a healthy product for human. In addition, the predictions using the FEEDNETICS™ indicated that the tested novel alternative formulations are economically viable. The validation of the model for fish growth resulted in a Mean Absolute Percentage Error (MAPE) of 8%.ConclusionsThe FEEDNETICS™ application enhances our ability to optimize feeding strategies and improve production efficiency in the aquaculture industry. VeraMaris® algae oil and PBM could serve as viable and sustainable raw materials for fish feed, promoting environmentally friendly aquaculture practices.

Performance of a Protein Language Model for Variant Annotation in Cardiac Disease.

Genetic testing is a cornerstone in the assessment of many cardiac diseases. However, variants are frequently classified as variants of unknown significance, limiting the utility of testing. Recently, the DeepMind group (Google) developed AlphaMissense, a unique artificial intelligence-based model, based on language model principles, for the prediction of missense variant pathogenicity. We aimed to report on the performance of AlphaMissense, accessed by VarCardio, an open web-based variant annotation engine, in a real-world cardiovascular genetics center. All genetic variants from an inherited arrhythmia program were examined using AlphaMissense via VarCard.io and compared with the ClinVar variant classification system, as well as another variant classification platform (Franklin by Genoox). The mutation reclassification rate and genotype-phenotype concordance were examined for all variants in the study. We included 266 patients with heritable cardiac diseases, harboring 339 missense variants. Of those, 230 (67.8%) were classified by ClinVar as either variants of unknown significance or nonclassified. Using VarCard.io, 198 variants of unknown significance (86.1%, 95% CI, 80.9-90.3) were reclassified to either likely pathogenic or likely benign. The reclassification rate was significantly higher for VarCard.io than for Franklin (86.1% versus 34.8%, P<0.001). Genotype-phenotype concordance was highly aligned using VarCard.io predictions, at 95.9% (95% CI, 92.8-97.9) concordance rate. For 109 variants classified as pathogenic, likely pathogenic, benign, or likely benign by ClinVar, concordance with VarCard.io was high (90.5%). AlphaMissense, accessed via VarCard.io, may be a highly efficient tool for cardiac genetic variant interpretation. The engine's notable performance in assessing variants that are classified as variants of unknown significance in ClinVar demonstrates its potential to enhance cardiac genetic testing.

Poisson’s ratio-LambdaRho rock-physics templates and a study on sensitivity of different fluid indicators

Seismic fluid indicators are highly accurate when calibrated, but when the data is scarce, an interpreter must choose between different indicators. We investigate the data from multiple basins to understand the most effective fluid indicators for use. Unlike previous studies, the data comprises a large, equal, and statistically meaningful number of hydrocarbon and brine sands from different basins. We show that Poisson’s ratio-LambdaRho templates can be used to separate different facies in synthetic and real data and can be used as a useful crossplot interpretation tool. We develop a fluid indicator, as LambdaRho Poisson’s ratio multiplication [Formula: see text], which is highly sensitive to saturation and can be potentially used as a fizz-gas discriminator. We compare the sensitivity of different fluid indicators to hydrocarbon saturation by using Bhattacharyya distance, which provides a quantitative metric for how well different indicators separate hydrocarbon and brine sands, and it is a nonparametric measure unlike other fluid indicator scores developed in similar studies. Absolute properties derived from seismic by inversion are rarely available in regional studies, whereas relative elastic properties can be easily obtained and used. Following the concepts of elastic reflectivity vectors and geometry of intercept-gradient crossplots, we show how the reflectivity of different fluid indicators can be approximated from amplitude variation with offset (AVO) parameters at various chi ([Formula: see text]) angles, enabling an interpreter to use them even when inversion products are not available. Finally, we compare the effectiveness of relative elastic parameters on different AVO classes and show that no single attribute works best across all classes but for general screening purposes fluid factor and [Formula: see text] can be good choices. The findings of this study can help better characterization of fluids in exploration, appraisal, and development of hydrocarbons, and in other areas where monitoring produced and injected fluids is important like 4D seismic or carbon capture storage.

A discursive analysis about the dissemination of voice in the newspaper Estadão

This article aims to describe and interpret the meaning effects of the voices propagated in the Estadão newspaper, using the well-established interpretive tool of Discourse Analysis. To this end, the following Estadão articles, all from 2015, were chosen as corpus for this study, according to the criterion of having the voice lexical marker as an integral part of the text: "Tony Bennett reinvents himself in a new album" (ESTADÃO, 2015) and "Julião Pinheiro, son of Paulo César Pinheiro and Luciana Rabello, releases album" (Pennafort, 2015). The notions of conditions of production, discursive formation and interdiscourse, and their respective intertwinings, are used. The analytical vectors described above are applied to examine the discursivity invested in the sayings about the voices projected in the media, in the interpretive procedure. As one of the main results, it appears that the liberal discursive formation of the market predominates in both periodical products, forming the sayings about the voice and, through this expedient, pointing to a conservative aesthetic of the successful voice, in the media outlet in question, with his perception goes back to other voices in the entertainment industry.

The utility of MRI radiological biomarkers in determining intracranial pressure

Intracranial pressure (ICP) is a physiological parameter that conventionally requires invasive monitoring for accurate measurement. Utilising multivariate predictive models, we sought to evaluate the utility of non-invasive, widely accessible MRI biomarkers in predicting ICP and their reversibility following cerebrospinal fluid (CSF) diversion. The retrospective study included 325 adult patients with suspected CSF dynamic disorders who underwent brain MRI scans within three months of elective 24-h ICP monitoring. Five MRI biomarkers were assessed: Yuh sella grade, optic nerve vertical tortuosity (VT), optic nerve sheath distension, posterior globe flattening and optic disc protrusion (ODP). The association between individual biomarkers and 24-h ICP was examined and reversibility of each following CSF diversion was assessed. Multivariate models incorporating these radiological biomarkers were utilised to predict 24-h median intracranial pressure. All five biomarkers were significantly associated with median 24-h ICP (p < 0.0001). Using a pair-wise approach, the presence of each abnormal biomarker was significantly associated with higher median 24-h ICP (p < 0.0001). On multivariate analysis, ICP was significantly and positively associated with Yuh sella grade (p < 0.0001), VT (p < 0.0001) and ODP (p = 0.003), after accounting for age and suspected diagnosis. The Bayesian multiple linear regression model predicted 24-h median ICP with a mean absolute error of 2.71 mmHg. Following CSF diversion, we found pituitary sella grade to show significant pairwise reversibility (p < 0.001). ICP was predicted with clinically useful precision utilising a compact Bayesian model, offering an easily interpretable tool using non-invasive MRI data. Brain MRI biomarkers are anticipated to play a more significant role in the screening, triaging, and referral of patients with suspected CSF dynamic disorders.

B-119 Comparative performance of GPT-4 and CNV-ETLAI in extracting copy number variations from medical journals: Bridging the gap between large language models and specialized NLP tools in genomic data interpretation

Abstract Background Copy Number Variations (CNVs) are critical genetic markers in diversity and disease, yet their accurate extraction from medical literature remains challenging due to the complexity of genetic data. While specialized NLP models like CNV-ETLAI have been developed for this task, the advent of Large Language Models (LLMs) such as GPT-4 presents a potential alternative with broader applicability. This study evaluates the efficacy of GPT-4 against CNV-ETLAI in extracting CNVs from medical journal articles, aiming to enhance genetic research and clinical decision-making. Methods We configured GPT-4 to process and interpret medical journal PDFs, designing custom prompts for CNV information extraction. The performance of GPT-4 was benchmarked against CNV-ETLAI using a dataset of 146 true positive CNVs extracted from 23 journal articles. Performance metrics focused on accuracy in extracting CNVs from both text and tables, recognizing the importance of structured data interpretation in genomic analysis. Results CNV-ETLAI demonstrated superior accuracy, achieving a 98% success rate in CNV extraction, compared to GPT-4’s 49%. Specifically, CNV-ETLAI outperformed GPT-4 in table extraction accuracy (99% vs. 41.2%) and context extraction accuracy (96% vs. 63.2%). Despite GPT-4's lower performance, its capacity for improvement and adaptability was noted, indicating potential future applicability in medical data extraction. Conclusions The study highlights CNV-ETLAI's current superiority in extracting CNVs from medical texts, particularly in interpreting structured data like tables. However, the adaptability and potential for growth in LLMs like GPT-4 suggest they could soon become valuable tools for medical data extraction, offering a more versatile and powerful solution across a broader range of applications. The promise of LLMs, despite their current limitations, underscores the need for continued research and development in AI technologies for genomic data interpretation.

Development of a thermal diagnostic system for the SPARC tokamak.

The SPARC tokamak will have scrape-off layer parallel heat fluxes on the order of GW/m2. Managing power exhaust of this magnitude will be mandatory for a reactor-scale device. To enable this mission, a thermal diagnostic suite will be deployed to measure the in-vessel structural temperatures to ensure they do not exceed their design limits and to determine the spatial distribution and magnitude of energy deposited onto the first wall. Thermocouples and fiber Bragg gratings have been selected for their environmental compatibility and proven useful on other fusion devices. High-density thermocouple arrays in the divertor will have two spring-loaded thermocouples per divertor target tile, which are being used as calorimeters, and will look to resolve the temperature distribution within the tile due to a swept or static strike point. All systems will need to survive the vacuum vessel bake, set at a minimum plasma facing surface temperature of 350 °C, which presents a particularly challenging environment for the fiber-based subsystem. Along with this temperature design requirement, all the materials in the primary vacuum need to be ultra-high vacuum compatible, able to handle the expected neutron and gamma radiation, as well as tritium exposure, all of which restrict material options. Finally, due to the expected activated environment in SPARC, there will be little chance to replace defective sensors, so system resilience is ensured through toroidal redundancy, probe material selection, and mitigating the impact of common-mode failures. Initial testing of sensors show that intershot structural measurements are sufficiently captured with the raw output, but intrashot measurements of the plasma facing material requires model-based interpretive tools.

HIERARCHICAL NEURAL NETWORKS IN PREDICTING THE PROPERTIES OF OIL AND GAS RESERVOIRS BASED ON WELL AND SEISMIC DATA

This paper describes a technique for hierarchical neural networks based on the nearest neighbor method with preliminary clustering of the original training dataset and construction of a search cluster decision tree. This method is a promising alternative to neural network technologies with deep learning and has quite a few advantages: high learning rate, identification of objects with a low degree of similarity, and the ability to generalize and retrain. As shown by testing the hierarchical neural network method on real data from the West Siberian oil and gas province, predicting the oil saturation in the Vikulov suite interval is much faster and more efficient than inversion approaches to quantitative interpretation of seismic data while achieving fairly similar geological results. This characterizes the proposed method of hierarchical neural networks as an effective tool for the quantitative interpretation of seismic data to solve geological problems.

Countering essentialism in psychiatric narratives

ABSTRACT The practice of self-diagnosing, amplified by the spread of psychiatric knowledge through social media, has grown rapidly. Yet, the motivations behind this trend, and, critically, its psychological repercussions remain poorly understood. Self-ascribing a psychiatric label always occurs within a broader narrative context, with narratives serving as essential interpretive tools for understanding oneself and others.In this paper, we identify four principal motivators for people pursuing self-diagnosis, pertaining to 1. waiting time and cost of mental health resources, 2. recognition, 3. identity formation, and 4. destigmatization. We compare these motivators against the backdrops of psychiatric narratives, including DSM-based (operationalism), bio-medical, and Neurodiversity narratives, to evaluate the psychological implications of self-ascriptions. We show that while self-ascription aligns with many motivations, it also carries the risk of essentialism across all analyzed narratives, which can over-simplify and sometimes misrepresent people’s attempts to find meaning through psychiatric labels. Essentialism makes narratives less comprehensive, responsive, and resourceful.We contend that self-ascriptions are not inherently problematic; instead, they show the need for greater focus and understanding in psychiatric practices and the importance of incorporating people’s experiences and perspectives in clinical settings. However, both the narratives and the social climate from which they emerge may need adjustments to make self-ascriptions meaningful, comprehensive, and resourceful tools.