Word Count Research Articles

Assessing the Reproducibility of the Structured Abstracts Generated by ChatGPT and Bard Compared to Human-Written Abstracts in the Field of Spine Surgery: Comparative Analysis.

Due to recent advances in artificial intelligence (AI), language model applications can generate logical text output that is difficult to distinguish from human writing. ChatGPT (OpenAI) and Bard (subsequently rebranded as "Gemini"; Google AI) were developed using distinct approaches, but little has been studied about the difference in their capability to generate the abstract. The use of AI to write scientific abstracts in the field of spine surgery is the center of much debate and controversy. The objective of this study is to assess the reproducibility of the structured abstracts generated by ChatGPT and Bard compared to human-written abstracts in the field of spine surgery. In total, 60 abstracts dealing with spine sections were randomly selected from 7 reputable journals and used as ChatGPT and Bard input statements to generate abstracts based on supplied paper titles. A total of 174 abstracts, divided into human-written abstracts, ChatGPT-generated abstracts, and Bard-generated abstracts, were evaluated for compliance with the structured format of journal guidelines and consistency of content. The likelihood of plagiarism and AI output was assessed using the iThenticate and ZeroGPT programs, respectively. A total of 8 reviewers in the spinal field evaluated 30 randomly extracted abstracts to determine whether they were produced by AI or human authors. The proportion of abstracts that met journal formatting guidelines was greater among ChatGPT abstracts (34/60, 56.6%) compared with those generated by Bard (6/54, 11.1%; P<.001). However, a higher proportion of Bard abstracts (49/54, 90.7%) had word counts that met journal guidelines compared with ChatGPT abstracts (30/60, 50%; P<.001). The similarity index was significantly lower among ChatGPT-generated abstracts (20.7%) compared with Bard-generated abstracts (32.1%; P<.001). The AI-detection program predicted that 21.7% (13/60) of the human group, 63.3% (38/60) of the ChatGPT group, and 87% (47/54) of the Bard group were possibly generated by AI, with an area under the curve value of 0.863 (P<.001). The mean detection rate by human reviewers was 53.8% (SD 11.2%), achieving a sensitivity of 56.3% and a specificity of 48.4%. A total of 56.3% (63/112) of the actual human-written abstracts and 55.9% (62/128) of AI-generated abstracts were recognized as human-written and AI-generated by human reviewers, respectively. Both ChatGPT and Bard can be used to help write abstracts, but most AI-generated abstracts are currently considered unethical due to high plagiarism and AI-detection rates. ChatGPT-generated abstracts appear to be superior to Bard-generated abstracts in meeting journal formatting guidelines. Because humans are unable to accurately distinguish abstracts written by humans from those produced by AI programs, it is crucial to exercise special caution and examine the ethical boundaries of using AI programs, including ChatGPT and Bard.

The Reddit cannabis subjective highness rating scale: Applying computational social science to explore psychological and environmental correlates of naturalistic cannabis use.

Social media data provide unprecedented access to discussions of active, naturalistic, and often real-time cannabis use in an era of cannabis policy liberalization. The aim of this study was to explore psychological and environmental correlates of cannabis effects by applying computational social science approaches to a large dataset of unprompted reports of naturalistic cannabis use with corresponding self-reported numerical ratings of subjective highness. Post title text was extracted via the Pushshift dataset from N = 328,865 posts to the r/trees Reddit community, where posters self-assess and disclose how high they feel on a scale from 1 to 10 (M = 6.9, SD = 1.8). Structural topic modelling and Linguistic Inquiry and Word Count (LIWC) dictionary-based approaches were applied to identify (1) frequently discussed topics and (2) text indicative of 5 psychological processes (affective, social, cognitive, perceptual, biological), respectively, as well as to examine relationships between subjective highness and (1) topic prevalence and (2) psychological process word counts. A 40-topic model was selected for interpretation based on semantic coherence and exclusivity. The most discussed topics in a 40-topic model were characterized by references to smoking places, social contexts, positive affect, cognitive states, as well as food and media consumed. In LIWC dictionary analyses, words mentioning affective, social, and cognitive processes were referenced more often than perceptual or body processes. Posters reported greater subjective highness when using language that referred to in-person social environments and lower subjective highness when using language that referred to online social environments and positive affect psychological states. This examination of unprompted online reports of naturalistic cannabis use identified textual content referring to affect and to other people as being associated with perceived effects of cannabis. These affective and social aspects of the cannabis use experience were salient to active posters in this online community and should be integrated into experience sampling methods and behavioral pharmacology research, as well as public health messaging.

Natural Language Processing Approaches to Text Data Augmentation: A Computational Linguistic Analysis

In the context of Natural Language Processing (NLP) tasks, problems such as insufficient or skewed data are frequently encountered. One practical solution to this problem is to generate additional textual data. Text Data Augmentation (TDA) refers to small changes made to accessible text at the character, word, or sentence level to generate synthetic data that is subsequently inserted into data loaders to train the model. By producing synthetic data, models can learn from a larger range of instances and, hence, enhance their resilience and generalization skills. Despite the fact that the entire NLP community has extensively studied many NLP DA approaches, recent research on the subject suggests that the relationship between the several DA techniques now in use is not entirely known in practice. Therefore, this study applies and extends the advances of TDA to encounter and cover varied tools on multiple settings or contexts. To carry out a thorough practical implementation of NLP DA approaches, comparing the way they perform and highlighting some of the significant similarities and differences in these various scenarios, this work depends on different tools of easy data augmentation and neural-based augmentation. This study suggests that some typical DA techniques might not be suitable in some circumstances or text environments. Specifically, according to the initial results, the context and word count of a text may have a significant impact on the quality of the synthetic data.

Exploring barriers to care home research recruitment during the COVID-19 pandemic: The influence of social media recruitment posts and public sentiment.

Recruitment of care home staff to research studies is recognised as challenging. This was further exacerbated by the COVID-19 pandemic and the associated negative media portrayal of care home workers. Social media use has surged since the onset of COVID-19 lockdowns, offering a plausible approach to understanding the barriers to care home research recruitment and gaining insight into public perceptions of care home workers. To utilise comments from two Facebook recruitment posts to: 1) gain an understanding of potential barriers to recruitment of healthcare workers (HCWs) in UK care homes, and 2) explore public sentiment towards care home research and care homes in the context of the COVID-19 pandemic. This cross-sectional study analysed comments from two Facebook posts (available June-October 2021) advertising a separate study on psychological support for care staff during the pandemic. This study was situated within a larger investigation into the mental health and wellbeing of care home staff and employed both qualitative analysis and quantitative methods (word count and correlations between words used and between posts). Three themes were identified from the qualitative analysis: support, mistrust and blame. There was a greater use of words associated with support and negative emotive words in post 2. Post 2 comments featured significantly more choice words and first-person singular pronouns than post 1 which indicated a resentful sentiment from those who advocate freedom of choice and control. Discussion of mistrust towards researchers was most prominent in post 1 indicating the importance of relationship building between researchers and HCWs in UK care homes. With attribution to blame, there was a larger range of negative emotion words than positive emotion words. Taken together our findings offer novel insights into why recruitment to care home research during the pandemic including the use of social media might be problematic. Social media is a useful tool for recruitment but should not be considered as a one-time input. Researchers should pro-actively engage with the study population from the start using co-design with resident and public groups to support recruitment and ensure these populations are accurately represented within research.

Advanced optimization-based weighted features for ensemble deep learning smart occupancy detection network for road traffic parking

In day-to-day activities, the advanced technology like Internet of Things (IoT) emerges to improve the lifestyle of people. In metropolitan cities, real-time parking is the long-lasting problem that we face in our daily life activities. Urban parking regulation gained more attention because of its capability to diminish energy consumption, congested traffic, and manifestation. The parking space detection for vehicles in real-time is a crucial role for on-street parking control models where the data is delivered to the drivers through internet with the help of Global Positioning System (GPS). Hence, the network congestion gets increased, where the existing model does not performed well in the parking space effectively. This research paper recommends a novel strategy for detecting occupancy in road traffic parking by IoT and deep networks to overcome these difficulties. At first, the required information is gathered from the standard datasets as CNRPark + EXT for further processing. After the data collection process, the preprocessing phase is executed, where the data cleaning and data transformation approaches are done with a multi-scale retinex network. The pre-processing technique is effectively performed to reduce background noise. Further, the feature extraction is progressed through the Residual Attention Network, where the relevant features are extracted from the pre-processed data. Subsequently, the optimal features and weights are selected by the novel Advanced Pelican Optimization Algorithm (APOA) to get the optimal weighted feature selection. This optimal weighted feature is further given to the Ensemble Deep Networks (EDN) by integrating the Deep Conditional Random Field (DCRF), and Extreme Deep Learning (EDL) model. In final process, the detection of occupancy in road traffic parking is determined by averaging both classifiers outcomes. Finally, the results are compared with numerous optimization algorithms and classifiers to ensure the developed system's efficacy. Throughout the validation, the developed model outperforms with an accuracy as 96.6, precision as 92.9 and Mathew's Correlation Coefficient (MCC) as 92.3. Thus, the developed model shows better performance than the existing traditional approaches.

Range-limited Heaps’ law for functional DNA words in the human genome

Heaps’ or Herdan-Heaps’ law is a linguistic law describing the relationship between the vocabulary/dictionary size (type) and word counts (token) to be a power-law function. Its existence in genomes with certain definition of DNA words is unclear partly because the dictionary size in genome could be much smaller than that in a human language. We define a DNA word as a coding region in a genome that codes for a protein domain. Using human chromosomes and chromosome arms as individual samples, we establish the existence of Heaps’ law in the human genome within limited range. Our definition of words in a genomic or proteomic context is different from other definitions such as over-represented k-mers which are much shorter in length. Although an approximate power-law distribution of protein domain sizes due to gene duplication and the related Zipf’s law is well known, their translation to the Heaps’ law in DNA words is not automatic. Several other animal genomes are shown herein also to exhibit range-limited Heaps’ law with our definition of DNA words, though with various exponents. When tokens were randomly sampled and sample sizes reach to the maximum level, a deviation from the Heaps’ law was observed, but a quadratic regression in log–log type-token plot fits the data perfectly. Investigation of type-token plot and its regression coefficients could provide an alternative narrative of reusage and redundancy of protein domains as well as creation of new protein domains from a linguistic perspective.

Health information-seeking on Reddit, by people who use opioids

Introduction. In the most recent year that data is available, 101,035 people died from drug overdoses in the United States. For people who use drugs, obtaining treatment remains a significant barrier due to issues of fear, stigmatisation, and illegality. Some people turn to the anonymous social media site, Reddit, for obtaining health and harm reduction information. Method. In this study we analysed 2,748 health-related posts on the subreddit r/opiates over a three-month period. Analysis. Using a thematic analysis grounded in social constructivism, we categorised the data to determine the most frequent types of health questions asked by people who use opioids. We then used the text analysis software Linguistic Inquiry and Word Count to determine the emotional content of the posts. Results. Most of the posts were asking about harm reduction strategies and methods to reduce or quit opioid use. Conclusion. The text analysis demonstrated evidence of Foucault’s discourse theory. People who use opioids need more information on harm reduction strategies, and especially safely administering and withdrawing from them.

Forecasting the Spread of Sustainability Movement: Computational Analysis on Social Media Messages Promoting Climate Actions

Growing concerns about climate change have led to sustainability campaigns via social media. Few researchers have explored how public responses are influenced by social media messages about sustainability and what factors of these campaigns increase their success. This study examines how sustainability issues are communicated and what features of social media messages promote audience engagement by applying the information-quality and message-valence theories. This study used computational content analysis to collect and analyze tweets about a climate-action movement created by the United Nations for three years, from 2020 to 2022. Initially, 1,070,930 tweets were collected, and 59,181 tweets were retained to test the hypotheses. The results showed that the number of shares increased with high word count, the presence of hashtags, and visual content but decreased with URLs, a verified tag, and positive sentiment. Our findings extend the existing literature by elaborating the roles of information qualities in the context of social media messages about sustainability. This study provides communication practitioners with an equation model to predict shares of a climate-action campaign message on social media.

DICTIONARY-BASED COMPARATIVE STUDY OF THE CHINESE “个 (GÈ)” AND THE MALAY “BUAH” NUMERAL CLASSIFIERS

Classifiers are used for categorising nouns according to animacy, form, shape, and size. As one of the Austronesian languages, Malay is said to have an extensive numeral classifier system. Past studies have classified these two classifiers as general classifiers; however, an extensive study comparing the use of these two classifiers have not been attempted. This study aims to highlight the similarities and differences by comparing the Chinese classifier “个 (gè)” and the Malay “buah” using evidence from reference classifier dictionaries of the two languages. The findings show that while some similarities exist and both classifiers can be used interchangeably with some nouns, these classifiers are unique and have distinctive semantic usage. Both个 (gè) and buah are used for organisation, products of nature, buildings and places, and artifacts (things created by humans). However, only个 (gè) can be used for organs or body parts, humans, fruits and food, time, directions, and thought and intellectual products. In contrast, only buah is used for transportation, accessories, home furniture and appliances, publications, musical instruments, and performances.

Objective Linguistic Markers Associated with Callous-Unemotional Traits in Early Childhood

Background. Callous-unemotional (CU) traits are associated with interpersonal difficulties and risk for severe conduct problems (CP). The ability to communicate thoughts and feelings is critical to social success, with language a promising treatment target. However, no prior studies have examined objective linguistic correlates of childhood CU traits in early childhood, which could give insight into underlying risk mechanisms and novel target treatments. Methods. We computed lexical (positive emotion, sad, and anger words) and conversational (interruptions and speech rate) markers produced by 131 children aged 5–6 years (M = 5.98; SD = 0.54, 58.8% female) and their parents while narrating wordless storybooks during two online visits separated by 6–8 weeks (M = 6.56, SD = 1.11; two books, order counterbalanced). Audio recordings were diarized, time-aligned, and orthographically transcribed using WebTrans. Conversational markers were calculated using R and word frequencies were calculated using Linguistic Inquiry and Word Count (LIWC) software. We examined links between child CU traits and linguistic markers, and explored whether relationships were moderated by child sex. Results. Higher CU traits were associated with fewer positive emotion words produced by parents and children. Higher CU traits were also associated with greater concordance in the degree of interruptions and expression of anger emotion words by parents and children. Conclusions. Results suggest that objective linguistic correlates of CU traits are detectable during early childhood, which could inform adjunctive treatment modules that improve outcomes by precisely tracking and targeting subtle communication patterns.

Automatic Detection of Verbal Deception in Romanian With Artificial Intelligence Methods

Automatic deception detection is an important task with several applications in both direct physical human communication, as well as in computer-mediated one. The objective of this paper is to study the nature of deceptive language. The primary goal of this study is to investigate deception in Romanian written communication. We created a number of artificial intelligence models (based on Support Vector Machine, Random Forest, and Artificial Neural Network) to detect dishonesty in a topic-specific corpus. To assess the efficiency of the Linguistic Inquiry and Word Count (LIWC) categories in Romanian, we conducted a comparison between multiple text representations based on LIWC, TF-IDF, and LSA. The results show that in the case of datasets with a common subject such as the one we used regarding friendship, text categorization is more successful using general text representations such as TF-IDF or LSA. The proposed approach achieves an accuracy of the classification of 91.3%, outperforming the similar approaches presented in the literature. These findings have implications in fields like linguistics and opinion mining, where research on this subject in languages other than English is necessary. Keywords: Deception Detection, Text Classification, Natural Language Processing, Machine Learning.

Cultural values and the P-O fit: comparative NLP analysis of German online job advertisements

PurposeWithin the Person-Organization fit framework and Signalling Theory, this study investigates the performance of word dictionaries detecting cultural values in online job advertisements as one form of external communication of an organization. Based upon a merge of the dictionaries, a corporate value analysis of Germany is conducted.Design/methodology/approachThe study builds on a dataset (n &gt; 151 k) of online job advertisements which were scraped from a German job portal. It was pre-processed according to natural language processing standards. For analysing the values of an organization a dictionary based word count was applied. Therefore, the current state-of-the-art dictionaries were tested, and an enhanced dictionary was developed and translated from English to German. Finally, a cluster analysis was conducted.FindingsThis study supports the possibility of measuring cultural values in texts where the enhanced dictionary based on Ponitzovskiy shows the best results. It thereby supports the use of the Universal Value Structure model (Schwartz, 1992) as well as the Signalling Theory (Guest et al., 2021), that values spread across 10 core or 4 aggregated dimensions are communicated via online job advertisements. Finally, the study offers a profile of the German corporate culture average as well as 4 cultural clusters and separate organizations, all with different profiles.Originality/valueThis study develops an enhanced dictionary based on a large dataset of online job advertisements for analysing the external communication of values or culture of an organization for improving the Person-Organization fit.

The influence of President Trump's micro-expressions during his COVID-19 national address on viewers' emotional response.

This preregistered study replicates and extends studies concerning emotional response to wartime rally speeches and applies it to U.S. President Donald Trump's first national address regarding the COVID-19 pandemic on March 11, 2020. We experimentally test the effect of a micro-expression (ME) by Trump associated with appraised threat on change in participant self-reported distress, sadness, anger, affinity, and reassurance while controlling for followership. We find that polarization is perpetuated in emotional response to the address which focused on portraying the COVID-19 threat as being of Chinese provenance. We also find a significant, albeit slight, effect by Trump's ME on self-reported sadness, suggesting that this facial behavior served did not diminish his speech, instead serving as a form of nonverbal punctuation. Further exploration of participant response using the Linguistic Inventory and Word Count software reinforces and extends these findings.

Exploring the Use of Natural Language Processing to Understand Emotions of Trainees and Faculty Regarding Entrustable Professional Activity Assessments.

Background In medical education, artificial intelligence techniques such as natural language processing (NLP) are starting to be used to capture and analyze emotions through written text. Objective To explore the application of NLP techniques to understand resident and faculty emotions related to entrustable professional activity (EPA) assessments. Methods Open-ended text data from a survey on emotions toward EPA assessments were analyzed. Respondents were residents and faculty from pediatrics (Peds), general surgery (GS), and emergency medicine (EM), recruited for a larger emotions study in 2023. Participants wrote about their emotions related to receiving/completing EPA assessments. We analyzed the frequency of words rated as positive via a validated sentiment lexicon used in NLP studies. Specifically, we were interested if the count of positive words varied as a function of group membership (faculty, resident), specialty (Peds, GS, EM), gender (man, woman, nonbinary), or visible minority status (yes, no, omit). Results A total of 66 text responses (30 faculty, 36 residents) contained text data useful for sentiment analysis. We analyzed the difference in the count of words categorized as positive across group, specialty, gender, and being a visible minority. Specialty was the only category revealing significant differences via a bootstrapped Poisson regression model with GS responses containing fewer positive words than EM responses. Conclusions By analyzing text data to understand emotions of residents and faculty through an NLP approach, we identified differences in EPA assessment-related emotions of residents versus faculty, and differences across specialties.

Enhancing Diagnostic Support for Chiari Malformation and Syringomyelia: A Comparative Study of Contextualized ChatGPT Models

The rapidly increasing adoption of large language models in medicine has drawn attention to potential applications within the field of neurosurgery. This study evaluates the effects of various contextualization methods on ChatGPT's ability to provide expert-consensus aligned recommendations on the diagnosis and management of Chiari Malformation and Syringomyelia. Native GPT4 and GPT4 models contextualized using various strategies were asked questions revised from the 2022 Chiari and Syringomyelia Consortium International Consensus Document. ChatGPT-provided responses were then compared to consensus statements using reviewer assessments of 1) responding to the prompt, 2) agreement of ChatGPT response with consensus statements, 3) recommendation to consult with a medical professional, and 4) presence of supplementary information. Flesch-Kincaid, SMOG, word count, and Gunning-Fog readability scores were calculated for each model using the quanteda package in R. Relative to GPT4, all contextualized GPTs demonstrated increased agreement with consensus statements. PDF+Prompting and Prompting models provided the most elevated agreement scores of 19 of 24 and 23 of 24, respectively, versus 9 of 24 for GPT4 (p=.021, p=.001). A trend toward improved readability was observed when comparing contextualized models at large to ChatGPT4, with significant decreases in average word count (180.7 vs 382.3, p<.001) and Flesch-Kincaid Reading Ease score (11.7 vs 17.2, p=.033). The enhanced performance observed in response to ChatGPT4 contextualization suggests broader applications of large language models in neurosurgery than what the current literature indicates. This study provides proof of concept for the use of contextualized GPT models in neurosurgical contexts and showcases the easy accessibility of improved model performance.

Self-mediatisation and the format of Swedish parliamentary speeches: Speech length and political slogans, 1920–2019

Abstract In this article, we investigate traces of a news media logic in the Swedish parliamentary speeches from 1920 to 2019. Drawing on theories of mediatisation, we examine two aspects: the length of the speeches and repeated political slogans. Our analysis is based on a complete corpus of parliamentary records with annotated speeches. Speech length was measured based on word count, and the identification of slogans was based on repeated seven-word segments, filtered to exclude generic phrases. While it is difficult to draw any definitive conclusions about the influence of an external media logic, the speech length has dropped by 50 per cent since 1920. This change relates to new parliamentary procedures, and from the 1980s, with the explicit goal to attract the news media. Short and snappy political slogans have increased significantly since the 1990s. This development reflects previous research stating that sound bites are getting shorter.

Exploring AI-generated content and professional guidelines in cancer symptom management: A comparative analysis between ChatGPT and NCCN guidelines.

e13610 Background: Artificial intelligence (AI)-driven tools, like ChatGPT, have become widely-available sources for online health information. Limited research has explored the congruity between AI-generated content and professional treatment guidelines. This study seeks to compare recommendations for cancer-related symptoms generated from ChatGPT with guidelines from National Comprehensive Cancer Network (NCCN), a provider focused source requiring users to log in or register to access its recommendations. Utilizing a provider-focused source like NCCN serves as a benchmark to assess whether ChatGPT recommendations align with the standards typically endorsed by clinicians. Methods: We extracted treatment recommendations from four NCCN Supportive Care webpages (Cancer Pain, Antiemesis, Cancer-Related Fatigue, and Distress Management) and five subsections of the NCCN Palliative Care webpage (dyspnea, constipation, diarrhea, sleep disturbances, and anorexia/cachexia). We then entered "How can I reduce my cancer-related [symptom]" into ChatGPT 3.5 and extracted its recommendations. We calculated and compared word count and Flesch-Kincaid Grade Level readability for each NCCN and ChatGPT section. We completed a comparative content analysis focusing on recommendations for medications, consultations, and non-pharmacological strategies. Results: Across the nine NCCN Supportive Care and Palliative Care webpages, the mean word count was 2393.8 (SD=2601.4) and the mean Flesch-Kincaid Grade Level was 17.3 (SD=1.4) vs 382.4 (SD=29.6) and 11.6 (SD=0.8) for ChatGPT. The mean percent agreement between NCCN and ChatGPT recommendations was 44.6% (range 14.3%-81.8%). ChatGPT and NCCN guidelines shared fewer than half of their symptom-related recommendations in all but one section, fatigue. NCCN offered specific medication recommendations across all sections. ChatGPT's recommendations lacked the specificity observed in NCCN's guidelines including often not suggesting any medications. ChatGPT recommended specific medications in the shortness of breath and diarrhea sections that were not recommended by NCCN. NCCN's guidelines often did not include recommendations related to spirituality or palliative care consults, areas that ChatGPT addressed. Conclusions: While ChatGPT provides concise, accessible supportive care advice including many non-medical support recommendations, discrepancies with guidelines raise concerns for patient-facing symptom management recommendations. Overall, AI-generated content like ChatGPT can assist in providing preliminary information but should be researched in conjunction with comprehensive, evidence-based guidance provided by healthcare professionals. Healthcare providers should work with AI developers to ensure data sources are high-quality and accurate.

Assessing the missions and visions of NCI-designated cancer centers and their affiliated hospitals.

e23118 Background: Mission and vision statements (MVS) help convey an institution's identity and priorities. Missions describe objectives and responsibilities, while visions describe overarching directions for the future. Effectively communicating MVS is particularly important for cancer centers and hospitals seeking to inspire and reach patients and employees. We sought to assess the composition, readability, and topics addressed for MVS provided by NCI-designated cancer centers and their affiliated hospitals. Methods: We extracted MVS data from institutional websites for 65 NCI-designated cancer centers and 59 affiliated hospitals in 2023 (6 cancer centers did not have an affiliated hospital). We determined the composition of statements using word counts and time to read. We assessed the readability of statements using Flesch-Kincaid (FK) reading ease and grade level scores. We reviewed the MVS to determine the presence of four topics: equity, quality care, training, and research. We used descriptive statistics to compare these issues in MVS for cancer center versus the hospital. Results: Among the 65 cancer centers, we found mission statements for 93.9% (61/65) and vision statements for 63.1% (41/65). All affiliated hospitals provided a mission statement and 86.4% (51/59) had a vision statement. Data for composition, readability, and topics addressed for MVS are provided in the table. Overall, readability of MVS was difficult based on FK reading ease scores, with relatively high reading grade levels. Mission statements for cancer centers had a significantly lower reading ease and higher grade level compared with hospitals. In general, mission statements were slightly longer than vision statements, more frequently addressing the topics of quality care, training, and research. Compared with hospitals, cancer centers had a significantly lower frequency of mentioning the topic "training" in their MVS. Conclusions: We found that the majority of NCI-designated cancer centers report mission statements, but fewer provide vision statements. We demonstrated difficult readability for the MVS provided, highlighting a need to consider simpler diction and structure of language. Additionally, our work provides an overview of topics addressed for these statements including equity, quality care, training, and research. These topics underscore the values and priorities of an institution, and this review of MVS provides insights into the messaging that cancer centers communicate. [Table: see text]

EFL Learners’ English Writing Feedback and Their Perception of Using ChatGPT

This study aims to investigate the utilization of AI-assisted feedback by EFL college learners in their English writing. Specifically, the research sought to explore how writing assisted by ChatGPT improves students’ writing skills in comparison to peer feedback. Additionally, the study aimed to understand the learners’ perceptions regarding the use of ChatGPT in editing English writing. Participants were tasked with submitting their presentation scripts before their speaking exams. They received an instructional session on how to use the tool effectively and were told useful prompts for using ChatGPT. The collected writings were analyzed holistically by two experienced EFL instructors, and analytically by using Grammarly (2022), a free online grammar and spelling checker, to identify characteristics. The analysis revealed that there was a significant difference in holistic scores including content and organization. The experimental group who used the AI tool had significantly reduced grammatical and lexical errors. However, no significant difference between the groups was found in the word counts and use of vocabulary types. Additionally, reflections from participants indicates a positive attitude toward the use of AI-assisted feedback in English writing but there were some concerns about reliability and over-reliance. The study suggests pedagogical implications for the effective integration of AI-assistance in English writing based on these findings.

Corporate reporting behavior: Factors influencing the adoption of integrated reporting in India

Corporations have a responsibility to provide a fair view of their financial state to their stakeholders. To facilitate the reporting process, the IFRS recommends the integrated reporting framework, which firms may may adopt voluntarily. This study examines the motivations of management in voluntarily disclosing capitals-related information in annual reports of companies listed on the Nifty Metal Index and their willingness to adopt the IR framework. It also empirically investigates how a company’s long-term and short-term goals impact integrated reporting adoption. The Integrated Reporting Capitals Index (IRCIN) was constructed to study the reporting behavior of companies within the specified framework. The reporting patterns within the metal industry are analyzed by employing the fixed effects model. Results highlight the interconnectedness of financial and non-financial capital dimensions in corporate reporting practices, albeit with a strong emphasis on financial capital. The average word count describing the financial capital was high, while the intellectual capital was the lowest. Strategic management evaluated via cash flow from investing activities proved statistically significant at the 5% level. Similarly, short-term goals indicated by profit after tax were found to play a crucial role in encouraging corporations within the Nifty Metal Index to adopt the integrated reporting framework. Conversely, book value per share exhibited a negative coefficient, indicating a historical disconnect with the reporting entities. These insights suggest that the adoption of the integrated reporting framework by management is driven by strategic goals, short-term objectives, and overall company performance.