Assessing the Efficacy of Large Language Models in Health Literacy: A Comprehensive Cross-Sectional Study.

Enhanced health literacy in children has been empirically linked to better health outcomes over the long term; however, few interventions have been shown to improve health literacy. In this context, we investigate whether large language models (LLMs) can serve as a medium to improve health literacy in children. We tested pediatric conditions using 26 different prompts in ChatGPT-3.5, ChatGPT-4, Microsoft Bing, and Google Bard (now known as Google Gemini). The primary outcome measurement was the reading grade level (RGL) of output as assessed by Gunning Fog, Flesch-Kincaid Grade Level, Automated Readability Index, and Coleman-Liau indices. Word counts were also assessed. Across all models, output for basic prompts such as "Explain" and "What is (are)," were at, or exceeded, the tenth-grade RGL. When prompts were specified to explain conditions from the first- to twelfth-grade level, we found that LLMs had varying abilities to tailor responses based on grade level. ChatGPT-3.5 provided responses that ranged from the seventh-grade to college freshmen RGL while ChatGPT-4 outputted responses from the tenth-grade to the college senior RGL. Microsoft Bing provided responses from the ninth- to eleventh-grade RGL while Google Bard provided responses from the seventh- to tenth-grade RGL. LLMs face challenges in crafting outputs below a sixth-grade RGL. However, their capability to modify outputs above this threshold, provides a potential mechanism for adolescents to explore, understand, and engage with information regarding their health conditions, spanning from simple to complex terms. Future studies are needed to verify the accuracy and efficacy of these tools.

Predictability and Variation in Language Are Differentially Affected by Learning and Production.

General principles of human cognition can help to explain why languages are more likely to have certain characteristics than others: structures that are difficult to process or produce will tend to be lost over time. One aspect of cognition that is implicated in language use is working memory-the component of short-term memory used for temporary storage and manipulation of information. In this study, we consider the relationship between working memory and regularization of linguistic variation. Regularization is a well-documented process whereby languages become less variable (on some dimension) over time. This process has been argued to be driven by the behavior of individual language users, but the specific mechanism is not agreed upon. Here, we use an artificial language learning experiment to investigate whether limitations in working memory during either language learning or language production drive regularization behavior. We find that taxing working memory during production results in the loss of all types of variation, but the process by which random variation becomes more predictable is better explained by learning biases. A computational model offers a potential explanation for the production effect using a simple self-priming mechanism.

Quality of Large Language Model Responses to Radiation Oncology Patient Care Questions.

Importance: Artificial intelligence (AI) large language models (LLMs) demonstrate potential in simulating human-like dialogue. Their efficacy in accurate patient-clinician communication within radiation oncology has yet to be explored. Objective: To determine an LLM's quality of responses to radiation oncology patient care questions using both domain-specific expertise and domain-agnostic metrics. Design, Setting, and Participants: This cross-sectional study retrieved questions and answers from websites (accessed February 1 to March 20, 2023) affiliated with the National Cancer Institute and the Radiological Society of North America. These questions were used as queries for an AI LLM, ChatGPT version 3.5 (accessed February 20 to April 20, 2023), to prompt LLM-generated responses. Three radiation oncologists and 3 radiation physicists ranked the LLM-generated responses for relative factual correctness, relative completeness, and relative conciseness compared with online expert answers. Statistical analysis was performed from July to October 2023. Main Outcomes and Measures: The LLM's responses were ranked by experts using domain-specific metrics such as relative correctness, conciseness, completeness, and potential harm compared with online expert answers on a 5-point Likert scale. Domain-agnostic metrics encompassing cosine similarity scores, readability scores, word count, lexicon, and syllable counts were computed as independent quality checks for LLM-generated responses. Results: Of the 115 radiation oncology questions retrieved from 4 professional society websites, the LLM performed the same or better in 108 responses (94%) for relative correctness, 89 responses (77%) for completeness, and 105 responses (91%) for conciseness compared with expert answers. Only 2 LLM responses were ranked as having potential harm. The mean (SD) readability consensus score for expert answers was 10.63 (3.17) vs 13.64 (2.22) for LLM answers (P < .001), indicating 10th grade and college reading levels, respectively. The mean (SD) number of syllables was 327.35 (277.15) for expert vs 376.21 (107.89) for LLM answers (P = .07), the mean (SD) word count was 226.33 (191.92) for expert vs 246.26 (69.36) for LLM answers (P = .27), and the mean (SD) lexicon score was 200.15 (171.28) for expert vs 219.10 (61.59) for LLM answers (P = .24). Conclusions and Relevance: In this cross-sectional study, the LLM generated accurate, comprehensive, and concise responses with minimal risk of harm, using language similar to human experts but at a higher reading level. These findings suggest the LLM's potential, with some retraining, as a valuable resource for patient queries in radiation oncology and other medical fields.

Protein language model-embedded geometric graphs power inter-protein contact prediction.

Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions. Although significant improvement has been made in inter-protein contact prediction recently, there is still a large room for improving the prediction accuracy. Here we present a new deep learning method referred to as PLMGraph-Inter for inter-protein contact prediction. Specifically, we employ rotationally and translationally invariant geometric graphs obtained from structures of interacting proteins to integrate multiple protein language models, which are successively transformed by graph encoders formed by geometric vector perceptrons and residual networks formed by dimensional hybrid residual blocks to predict inter-protein contacts. Extensive evaluation on multiple test sets illustrates that PLMGraph-Inter outperforms five top inter-protein contact prediction methods, including DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter, by large margins. In addition, we also show that the prediction of PLMGraph-Inter can complement the result of AlphaFold-Multimer. Finally, we show leveraging the contacts predicted by PLMGraph-Inter as constraints for protein-protein docking can dramatically improve its performance for protein complex structure prediction.

BioBBC: a multi-feature model that enhances the detection of biomedical entities.

The rapid increase in biomedical publications necessitates efficient systems to automatically handle Biomedical Named Entity Recognition (BioNER) tasks in unstructured text. However, accurately detecting biomedical entities is quite challenging due to the complexity of their names and the frequent use of abbreviations. In this paper, we propose BioBBC, a deep learning (DL) model that utilizes multi-feature embeddings and is constructed based on the BERT-BiLSTM-CRF to address the BioNER task. BioBBC consists of three main layers; an embedding layer, a Long Short-Term Memory (Bi-LSTM) layer, and a Conditional Random Fields (CRF) layer. BioBBC takes sentences from the biomedical domain as input and identifies the biomedical entities mentioned within the text. The embedding layer generates enriched contextual representation vectors of the input by learning the text through four types of embeddings: part-of-speech tags (POS tags) embedding, char-level embedding, BERT embedding, and data-specific embedding. The BiLSTM layer produces additional syntactic and semantic feature representations. Finally, the CRF layer identifies the best possible tag sequence for the input sentence. Our model is well-constructed and well-optimized for detecting different types of biomedical entities. Based on experimental results, our model outperformed state-of-the-art (SOTA) models with significant improvements based on six benchmark BioNER datasets.

How children generalize novel nouns: An eye-tracking analysis of their generalization strategies.

Recent research has shown that comparisons of multiple learning stimuli which are associated with the same novel noun favor taxonomic generalization of this noun. These findings contrast with single-stimulus learning in which children follow so-called lexical biases. However, little is known about the underlying search strategies. The present experiment provides an eye-tracking analysis of search strategies during novel word learning in a comparison design. We manipulated both the conceptual distance between the two learning items, i.e., children saw examples which were associated with a noun (e.g., the two learning items were either two bracelets in a "close" comparison condition or a bracelet and a watch in a "far" comparison condition), and the conceptual distance between the learning items and the taxonomically related items in the generalization options (e.g., the taxonomic generalization answer; a pendant, a near generalization item; versus a bow tie, a distant generalization item). We tested 5-, 6- and 8-year-old children's taxonomic (versus perceptual and thematic) generalization of novel names for objects. The search patterns showed that participants first focused on the learning items and then compared them with each of the possible choices. They also spent less time comparing the various options with one another; this search profile remained stable across age groups. Data also revealed that early comparisons, (i.e., reflecting alignment strategies) predicted generalization performance. We discuss four search strategies as well as the effect of age and conceptual distance on these strategies.

Dementia-Related Disparities in Adult Day Centers: Results of a Bivariate Analysis.

PURPOSE: Adult day services (ADS) are a valuable resource for people living with Alzheimer's disease and Alzheimer's disease and related dementias (AD/ADRD) and serve a large population of late-life immigrants, often with limited English proficiency (LEP). This secondary data analysis examined potential disparities in diagnosis, dementia severity, medical complexity, and dementia-related behavioral problems in persons with AD/ADRD with LEP within the ADS setting. METHOD: The current study used data from TurboTAR, the electronic health record for ADS in California. Bivariate analyses were conducted to examine differences in clinical management for those with and without LEP. RESULTS: Of 3,053 participants included in the study, 42.3% had LEP. Participants with LEP had higher rates of emergency department use and medication mismanagement. However, due to non-standard data collection, there was a significant amount of missing data on language preference (38.1%) and race/ethnicity (46.5%). Although these findings suggest LEP may play a role in the clinical management of persons with AD/ADRD in ADS, missing data caused by lack of standardized collection compromise the results. CONCLUSION: It is essential to improve data collection practices in ADS on language, race, and ethnicity to help identify health disparities and promote equitable care for marginalized older adults. [Journal of Gerontological Nursing, 50(4), 42-47.].

Utilization of Mental Health Services Within 1 Year of Delivery among Different Language Groups.

OBJECTIVES: Cultural differences can affect postpartum mental health disorders and the utilization of mental health services. We compared women speaking English, Spanish, Russian, and Urdu/Bengali/Punjabi from postpartum through 1 year after delivery. METHODS: This was a retrospective study of 3478 pregnant women from a public hospital in New York City. The primary outcome was a composite outcome of the presence of any of the following: diagnosis of depressive disorder, diagnosis of anxiety disorder, visit to a behavioral health service provider, and/or psychiatric admission. The secondary outcome was healthcare provider referral to a behavioral health service provider. RESULTS: Languages spoken were English (n = 1881), Spanish (n = 694), Russian (n = 600), and Urdu/Bengali/Punjabi (n = 303). The language groups differed significantly (P = 0.02) for the composite outcome, with English having the greatest percentage (3.5%) and Russian the lowest percentage (1.2%). The language groups significantly differed for referral to behavioral health (P = 0.04), with Spanish having the greatest percentage (1.6%) and Russian the lowest percentage (0.2%). Anxiety disorder history (odds ratio [OR] 10.43, 95% confidence interval [CI] 4.75-22.91, P < 0.001) and psychiatric disorder history (OR 5.26, 95% CI 2.13-8.49, P < 0.001) were each significantly associated with increased odds for the composite outcome. Anxiety disorder history (OR 6.42, 95% CI 1.92-21.45, P = 0.003) and elevated depressive symptoms (OR 4.92, 95% CI 2.04-11.83, P < 0.001) each were significantly associated with increased odds for referral to behavioral health. CONCLUSIONS: Russian language was associated with lower utilization of mental health services postpartum. These findings can help clinicians determine among postpartum women who will be affected with mental health concerns and who will seek treatment for mental health concerns.

[The use of standardized nursing languages in electronic medical records: an exploratory study on opportunities, limitations, and strategies]. / L'uso del linguaggio standardizzato infermieristico nelle cartelle cliniche elettroniche: studio esplorativo su opportunità, limiti e strategie.

. The use of standardized nursing languages in electronic medical records: an exploratory study on opportunities, limitations, and strategies. INTRODUCTION: Standardized nursing languages (SNLs) have found increasing application in electronic medical records in recent years. In Italy their use is still uneven and accompanied by a silent debate between positions 'against' and 'for' their use. AIM: To render visible the debate regarding SNLs in Italy, and the strategies to consider when digitized records are based on a SNL. METHOD: Data has been collected through audio-recorded semi-structured interviews, selecting three Italian nursing professors, four managers representing Italian healthcare settings that used a SNT and a representative of the Central committee of the National federation of orders of nursing professions. The thematic approach was used to analyze the data. RESULTS: Participants reported having introduced digitized records based on nursing diagnoses, integrated with the Nursing Interventions Classification System and Nursing Outcome Classification, Clinical Care Classification System, Nursing Sensitive Outcomes or mixed models. Divergent aspects emerge regarding: (1) using nursing languages vs a common language to other healthcare professions; (2) planning care vs enhancing clinical reasoning; (3) measuring nursing care vs accepting the variability of the practice, and (4) making documentation efficient vs dedicating more time. Some convergences have emerged and a set of indications for introducing electronic records when based on standardized languages. CONCLUSIONS: The introduction of electronic documentation requires the use of homogeneous languages. The debate on the potential and limits of SNL is still open and requires reflection among researchers, trainers, clinicians, and coordinators/managers of nursing care regarding the choices to be made which may have long-term effects on many nurses.

English vowel recognition in multi-talker babbles mixed with different numbers of talkersa).

The present study examined English vowel recognition in multi-talker babbles (MTBs) in 20 normal-hearing, native-English-speaking adult listeners. Twelve vowels, embedded in the h-V-d structure, were presented in MTBs consisting of 1, 2, 4, 6, 8, 10, and 12 talkers (numbers of talkers [N]) and a speech-shaped noise at signal-to-noise ratios of -12, -6, and 0 dB. Results showed that vowel recognition performance was a non-monotonic function of N when signal-to-noise ratios were less favorable. The masking effects of MTBs on vowel recognition were most similar to consonant recognition but less so to word and sentence recognition reported in previous studies.

[From comprehensive geriatric assessment to an integrated multidimensional assessment system : interRAI tools and their applications]. / De l'évaluation gériatrique standardisée à un système intégré d'évaluation multidimensionnelle : les outils interRAI et leurs applications.

The multidimensional assessment carried out with interRAI tools constitutes an operationalization of the International Classification of Functioning, Disability and Health (ICF) and is adapted to the specificities of each place of care. From a single assessment, the interRAI approach makes it possible to conduct a multidimensional assessment of functional autonomy and to produce a series of indicators (health, areas of intervention, quality of care and consumption of resources). It helps to identify clinical needs to be the subject of a personalized care plan and the strengths and weaknesses of health organizations to modify the professional practices. Compared to standardized geriatric assessment, interRAI tools consider the person's expectations and resources, offer a universal common language, produce a multidimensional synthesis and facilitate the construction of an integrated information system. The basis for their development is scientificity based on evidence.

Performance of ChatGPT on Chinese Master's Degree Entrance Examination in Clinical Medicine.

BACKGROUND: ChatGPT is a large language model designed to generate responses based on a contextual understanding of user queries and requests. This study utilised the entrance examination for the Master of Clinical Medicine in Traditional Chinese Medicine to assesses the reliability and practicality of ChatGPT within the domain of medical education. METHODS: We selected 330 single and multiple-choice questions from the 2021 and 2022 Chinese Master of Clinical Medicine comprehensive examinations, which did not include any images or tables. To ensure the test's accuracy and authenticity, we preserved the original format of the query and alternative test texts, without any modifications or explanations. RESULTS: Both ChatGPT3.5 and GPT-4 attained average scores surpassing the admission threshold. Noteworthy is that ChatGPT achieved the highest score in the Medical Humanities section, boasting a correct rate of 93.75%. However, it is worth noting that ChatGPT3.5 exhibited the lowest accuracy percentage of 37.5% in the Pathology division, while GPT-4 also displayed a relatively lower correctness percentage of 60.23% in the Biochemistry section. An analysis of sub-questions revealed that ChatGPT demonstrates superior performance in handling single-choice questions but performs poorly in multiple-choice questions. CONCLUSION: ChatGPT exhibits a degree of medical knowledge and the capacity to aid in diagnosing and treating diseases. Nevertheless, enhancements are warranted to address its accuracy and reliability limitations. Imperatively, rigorous evaluation and oversight must accompany its utilization, accompanied by proactive measures to surmount prevailing constraints.

Spontaneous perspective-taking in real-time language comprehension: evidence from eye-movements and grain of coordination.

Linguistic communication requires interlocutors to consider differences in each other's knowledge (perspective-taking). However, perspective-taking might either be spontaneous or strategic. We monitored listeners' eye movements in a referential communication task. A virtual speaker gave temporally ambiguous instructions with scalar adjectives ("big" in "big cubic block"). Scalar adjectives assume a contrasting object (a small cubic block). We manipulated whether the contrasting object (a small triangle) for a competitor object (a big triangle) was in common ground (visible to both speaker and listener) or was occluded so it was in the listener's privileged ground, in which case perspective-taking would allow earlier reference-resolution. We used a complex visual context with multiple objects, making strategic perspective-taking unlikely when all objects are in the listener's referential domain. A turn-taking, puzzle-solving task manipulated whether participants could anticipate a more restricted referential domain. Pieces were either confined to a small area (requiring fine-grained coordination) or distributed across spatially distinct regions (requiring only coarse-grained coordination). Results strongly supported spontaneous perspective-taking: Although comprehension was less time-locked in the coarse-grained condition, participants in both conditions used perspective information to identify the target referent earlier when the competitor contrast was in privileged ground, even when participants believed instructions were computer-generated.

Artificial intelligence large language model ChatGPT: is it a trustworthy and reliable source of information for sarcoma patients?

Introduction: Since its introduction in November 2022, the artificial intelligence large language model ChatGPT has taken the world by storm. Among other applications it can be used by patients as a source of information on diseases and their treatments. However, little is known about the quality of the sarcoma-related information ChatGPT provides. We therefore aimed at analyzing how sarcoma experts evaluate the quality of ChatGPT's responses on sarcoma-related inquiries and assess the bot's answers in specific evaluation metrics. Methods: The ChatGPT responses to a sample of 25 sarcoma-related questions (5 definitions, 9 general questions, and 11 treatment-related inquiries) were evaluated by 3 independent sarcoma experts. Each response was compared with authoritative resources and international guidelines and graded on 5 different metrics using a 5-point Likert scale: completeness, misleadingness, accuracy, being up-to-date, and appropriateness. This resulted in maximum 25 and minimum 5 points per answer, with higher scores indicating a higher response quality. Scores ≥21 points were rated as very good, between 16 and 20 as good, while scores ≤15 points were classified as poor (11-15) and very poor (≤10). Results: The median score that ChatGPT's answers achieved was 18.3 points (IQR, i.e., Inter-Quartile Range, 12.3-20.3 points). Six answers were classified as very good, 9 as good, while 5 answers each were rated as poor and very poor. The best scores were documented in the evaluation of how appropriate the response was for patients (median, 3.7 points; IQR, 2.5-4.2 points), which were significantly higher compared to the accuracy scores (median, 3.3 points; IQR, 2.0-4.2 points; p = 0.035). ChatGPT fared considerably worse with treatment-related questions, with only 45% of its responses classified as good or very good, compared to general questions (78% of responses good/very good) and definitions (60% of responses good/very good). Discussion: The answers ChatGPT provided on a rare disease, such as sarcoma, were found to be of very inconsistent quality, with some answers being classified as very good and others as very poor. Sarcoma physicians should be aware of the risks of misinformation that ChatGPT poses and advise their patients accordingly.

The Contribution of Willingness to Communicate to L2 Learners' Depth of Vocabulary Knowledge: An Empirical Study.

The issues of depth vocabulary knowledge and Willingness to Communicate (henceforth, WTC) are among the most important issues in second language learning. The present study set out to empirically look into the contribution of WTC to depth of vocabulary knowledge in L2 learning. To this end, 88 English L2 learners, divided into two groups in terms of their WTC, were given two depth vocabulary tests. The Word Association Test (WAT) was first administered to make a comparison between the depth vocabulary knowledge of the two WTC groups. Then, to triangulate the results, the Word Part Levels Test (WPLT) was administered to check whether the obtained results confirmed those of WAT. Analyzing data through independent t-test and MANOVA indicated that learners with higher levels of WTC had deeper vocabulary knowledge than those with lower levels of WTC on the WAT. Further, the triangulation results evinced that although the two groups did not differ significantly on the form-section and meaning-section of the WPLT, they significantly differed on the use-section of the test. The relevant pedagogical implications of the study are discussed.

Universal and Language-Specific Connected Speech Characteristics of Bilingual Speakers With Alzheimer's Disease: Insights From Case Studies of Structurally Distinct Languages.

PURPOSE: Connected speech analysis has been effectively utilized for the diagnosis and disease monitoring of individuals with Alzheimer's disease (AD). Existing research has been conducted mostly in monolingual English speakers with a noticeable lack of evidence from bilinguals and non-English speakers, particularly in non-European languages. Using a case study approach, we characterized connected speech profiles of two Bengali-English bilingual speakers with AD to determine the universal features of language impairments in both languages, identify language-specific differences between the languages, and explore language impairment characteristics of the participants with AD in relation to their bilingual language experience. METHOD: Participants included two Bengali-English bilingual speakers with AD and a group of age-, gender-, education-, and language-matched neurologically healthy controls. Connected speech samples were collected in first language (L1; Bengali) and second language (L2; English) using a novel storytelling task (i.e., Frog, Where Are You?). These samples were analyzed using an augmented quantitative production analysis and correct information unit analyses for productivity, fluency, syntactic and morphosyntactic features, and lexical and semantic characteristics. RESULTS: Irrespective of the language, AD impacted speech productivity (speech rate and fluency) and semantic characteristics in both languages. Unique language-specific differences were noted on syntactic measures (reduced sentence length in Bengali), lexical distribution (fewer pronouns and absence of reduplication in Bengali), and inflectional properties (no difficulties with noun or verb inflections in Bengali). Among the two participants with AD, the individual who showed lower proficiency and usage in L2 (English) demonstrated reduced syntactic complexity and morphosyntactic richness in English. CONCLUSIONS: Evidence from these case studies suggests that language impairment features in AD are not universal across languages, particularly in comparison to impairments typically associated with language breakdowns in English. This study underscores the importance of establishing connected speech profiles in AD for non-English-speaking populations, especially for structurally different languages. This would in turn lead to the development of language-specific markers that can facilitate early detection of language deterioration and aid in improving diagnosis of AD in individuals belonging to underserved linguistically diverse populations. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.25412458.

"The right we have to our owne bodies, goods, and liberties": The Freedom of the Ancient Constitution and Common Law in Milton's Early Prose.

Scholars have long recognized the importance of liberty in Milton's early prose, but they tend to center their analysis on republicanism. Although he would go on to express republicanism, Milton's early tracts tie liberty to English political and legal traditions rather than classical ones. Milton, in his early tracts, utilizes the language of the ancient constitution and the common law as he centers liberty on the property and bodies of English citizens, thus framing liberty in distinctly English terms. Additionally, Milton's early prose accepts the power of the monarch, revealing Milton's initial commitment to the existing political structure.

Dysphagia Service Delivery in the Educational Setting: Pediatric Feeding Disorder and the Culturally Responsive Clinician.

PURPOSE: This article addresses considerations for the speech-language pathologist to ensure culturally competent dysphagia management in the school setting for children with oral motor, swallowing, and pediatric feeding disorders (PFDs). There is also discussion of the multifactorial cultural and linguistic influences that impact collaborative educational decisions when establishing and implementing school-based dysphagia services. CONCLUSIONS: The consideration of cultural and linguistic factors for the child with oral motor, swallowing, and/or PFDs is essential when diagnosing, treating, and planning for dysphagia service delivery. By recognizing and including culturally appropriate interventions and recommendations, speech-language pathologists enhance opportunities for positive outcomes and treatment efficacy when providing pediatric dysphagia services in the educational setting for children from culturally and linguistically diverse backgrounds.

Pediatric Feeding Disorder and the School-Based Speech-Language Pathologist: An Evidence-Based Update for Clinical Practice.

PURPOSE: This prologue introduces the forum "Pediatric Feeding Disorder and the School-Based SLP: An Evidence-Based Update for Clinical Practice" and informs the reader of the scope of articles presented. METHOD: The guest prologue author provides a brief history of pediatric feeding and swallowing services in the public-school setting, including previous forums on swallowing and feeding services in the schools (Logemann & O'Toole, 2000; McNeilly & Sheppard, 2008). The concepts that have been learned since the 2008 forum are shared. The contributing authors in the forum are introduced, and a summary is provided for each of the articles. CONCLUSIONS: The articles provide evidence-based information on topics that are uniquely of interest to school-based speech-language pathologists managing pediatric feeding and swallowing in their districts. The topics shared in this forum range from relevant information on anatomy, physiology, developmental milestones, and differential diagnosis to therapeutic practice when identifying and treating pediatric feeding and swallowing in the school setting. The forum also includes focused articles on the necessity of collaboration with families during the treatment process, current information on legal parameters dealing with school-based pediatric feeding disorder services, and a framework for assessment and treating pediatric feeding disorder in the school setting.