Relevant Words Research Articles

P-465 A systematic review and meta-analysis of ovarian stimulation outcomes in cancer patients opting for oocyte cryopreservation prior to treatment compared to social egg-freezers

Abstract Study question Do patients with malignancy undertaking oocyte cryopreservation (OC) prior to cancer treatment have comparable cycle outcomes to patients having OC for social reasons? Summary answer Cancer patients achieve similar oocyte yields following ovarian stimulation to social freezers. However, they express lower peak oestradiol (E2), antral follicle count(AFC), and anti-Mullerian hormone(AMH). What is known already Increasing numbers of women are diagnosed with cancer in their reproductive years. Medical advances have led to earlier detection, improved treatments, and higher survival rates. However, cytotoxic cancer therapy is known to have a negative impact on ovarian reserve and fertility. Oocyte cryopreservation prior to such treatments provides a means of preserving fertility. Statistics on cycle outcomes often comes from social egg-freezers, however it is not yet known if patients with malignancy opting to preserve oocytes prior to treatment can expect the same results as their healthy counterparts. Study design, size, duration A literature search was performed on databases MEDLINE, EMBASE and Web of Science. Relevant text words and MeSH headings were used to identify all relevant papers. Studies reporting cycle outcomes of either oncology patients or social egg-freezers, or compared both were included. Only manuscripts that reported the number of mature oocytes obtained through controlled ovarian stimulation were included. Participants/materials, setting, methods All patients presenting for oocyte cryopreservation prior to cancer therapy were included with no distinction made between cancer type. Controls included women presenting for oocyte cryopreservation for social reasons. Those presenting for male-factor infertility were not included. Data extracted included patient age total number of oocytes retrieved, peak E2, AFC, AMH, gonadotropin dose, and days of stimulation. Statistical heterogeneity was calculated and outcomes were presented to a 95% confidence interval (CI). Main results and the role of chance A search of the databases retrieved 3805 articles, 116 of which remained for full-text evaluation after duplicates were removed and article titles and abstracts were assessed. Twenty-two articles met the inclusion criteria and were included in the meta-analyses. Total number of oocytes retrieved was similar between both groups with no significant difference in the means [standard mean difference mean difference (SMD) 1.069, 95% CI -1.1169 to 3.2549]. Peak E2, AFC, and AMH were all significantly lower in cancer patients with a SMD of -1031.4 (95% CI -1805.2 to -257.7), 0.277 (95% CI 0.042 to 0.511), and -2.289 (95% CI -3,913 to -0.665) respectively. There was no significant difference observed in number of mature (M2) oocytes, total gonadotropin dose or number of stimulation days with an SMD of -0.022 (95% CI -1.861 to 1.817), 188.75 (95% CI- 432.86 to 810.35), and 0.002 (95% CI -0.439 to 0.443) respectively. Limitations, reasons for caution Population characteristics, age, and BMI (major factors in fertility prognostics) were inconsistent between studies. Differences in cancer type, stage, grade, and stimulation protocols also potentially contributed to high heterogeneity. Few studies report obstetric outcomes, which could not be evaluated. Live-birth rate has arguably greater clinical significance but requires improved reporting. Wider implications of the findings This research supports the view that cancer negatively impacts ovarian reserve and supports an argument for improved patient access to fertility preservation. As clinical practice improves cancer survivor rates, a responsibility lies with healthcare professionals to provide evidence-based and accurate counselling for patients to make informed decisions about their fertility. Trial registration number Not applicable

ARAP DİLİ LİTERATÜRÜNDE LAḤN OLGUSUNUN TESPİT VE TASHÎHİNDE KULLANILAN KAYNAKLAR

Morphologically, the infinitive of the verb لحَنَ يَلْحَنُ the concept of “اللِحْنُ /al-laḥn”, lexicographic tune, composition, melody, harmonious sound that is pleasing to the ear; It has different meanings, such as speaking incorrectly and speaking in a closed and pseudonymous way that no one else can understand. In the Arabic language terminology, the concept in question is described as the opposite of fluent Arabic and in the pronunciation of letters or words; in the meaning and use of words; it means to make mistakes in the formation of the sentence and in the irapta/movements. The phenomenon of laḥn, which can be briefly defined as the incorrect use in the language, is a subject that needs to be examined in many ways. In this study, the subject of Detection of the Laḥn Phenomenon in the Arabic Language Literature and Sources Used in Tasḥîḥ has been examined. It is beyond argument that language, which is one of the hallmarks of human beings, is in a state of constant change as a living entity by nature, depending on both cultural, economic and technological developments. Functionally, language is the most vital part of life, the basic element of literature and national culture, and has an important place in human life. In addition to these, as a means of communication and expression, it has features such as approximator and fuser. Because of these features, peoples have attached importance to its correct development. Arabs, who had a distinguished place in the pre-Islamic period with their fluency and eloquence, clearly put language and literature above everything else and considered this a source of pride. With the disposition they inherited from their ancestors, they could easily understand which word signified which meaning. They did not need any grammatical rules to correct or strengthen the selica. Along with Islam - especially with the people's influx of people entering Islam in groups as a result of the conquests - the contact of Arabs with many foreign cultures, and the widespread use of laḥn in the Arabic language began to appear in almost every part of the society and in many different fields. It is known that this situation played a leading role in laying the foundation of the Arabic grammar and starting the dictionary studies in the eyes of the scholars of the period. But II/VII. Since the middle of the century, studies such as lexicon and grammar were not sufficient and the phenomenon of laḥn continued to become widespread, and the "Laḥnu'l-âmme, Laḥnu'l-hâṣṣe, Mâ telhânü fîhi'l-ʿamme, Laḥnu'l-avâm" aimed at detecting and correcting erroneous uses. and laḥnu'l-havâṣ”, many works have been written. Ali b. Hamza al-Kisâî's (d. 189/805) Mâ telḥanü fîhi'l-ʿavâm, and Ibn al-Sikkît's (d. 244/858) Iṣlâḥu'l-manṭık; Mufaddal b. Mâ telḥanü fîhi'l-ʿamme of Salama (d. 290/903); Kitâbü'l-Faṣîḥ by Abu'l-Abbas Sa'leb (d. 291/904); Leyse fî kalami'l-ʿArab of Ibn Halaveyh al-Hamedani (d. 370/980); Abu Bakr al-Zubaidi's (d. 379/989) Laḥnü'l-ʿavâm; The Tes̱ḳīfü'l-lisân of Ibn Makkî (d. 501/1108); Abu Muhammad al-Harîrî's (d. 516/1122) Dürretü'l-Şavvâṣ fî evhâmi'l-ḫavâṣ; Ebû Mansûr al-Cevâlîkî's (d. 540/1145) Tekmileti îṣlâḥ mâ taġleṭu fîhi'l-ʿâmme; Ibn Hisham al-Lahmi's (d. 577/1181) al-Medḫal to taḳvîmi'l-lisân; Ibn Berrî al-Maqdisî's (d. 582/1187) Ġalaṭü'ḍ-ḍuʿafâʾ mine'l-fuḳahâʾ; Works such as İbnü'l-Cevzî's (d. 597/1201) Calendarü'l-lisân and Ebü's-Safâ es-Safedî's (d. 764/1363) Taṣḥîḥü't-taṣḥîf are some of the works written in this field. are important works. Recently, some works have been written on the detection and correction of the phenomenon of laḥn in the Arabic language. Tehẕîbü'l-elfâẓi'l-'âmmiyye by Muhammad Ali ed-Dusûkî; Teẕkiretü'l-kâtib of Es'ad Halîl Dâğir, Aḫtâunâ fi's- ṣuḥûf ve'd-devâvîn of Salâhuddîn ez-Ze'balâvî; Abdulkadir al-Mağribî's Asarâtü'l-lisân fi'l-luga; Works such as Muhammad al-Adnânî's Mu'cemü'l-ağlâṭi'l-lükeviyya al-muâṣıra and Şevkî Dayf's Taḥrifâtü'l-'âmmiyye li'l-füṣḥâ are some important works that stand out in this context. are studies. The authors in question tried to prove the authentic format based on some sources, not randomly, while detecting and correcting the errors they observed. Although the quantity and quality vary from person to person, these sources are generally Arabic poetry, the Qur'an, recitations, hadiths, fluent Arabic prose, the lexical meaning of the relevant word or phrase, and Arabic grammar.

ENRICHMENT OF LANGUAGES IN THE PROCESS OF LANGUAGE RELATIONS

The paper describes the enrichment of languages in language contacts. The study has shown that the key reason for borrowing words by languages is the lack of an appropriate word to express any subject, thing, process, event, or concept in them. To express a concept in the absence of a relevant word, a new word should be either created using the language’s own word-formation capabilities or borrowed from another language. It is defined that borrowing not only fills empty semantic ‘voids’ in the language but also sometimes acts as either a new variant of a certain word or a terminological unit. Such terms belong to different terminological fields and play a unique role in enriching the language vocabulary. The term first emerges in one language and then passes from one language to another, expanding its application area.

Verbal Art in Luwian and Hittite Incantations

Abstract The present article wants to draw attention to the artful wordplay featured in the Cuneiform Luwian incantations known from Hittite Anatolia in the 2nd millennium BCE. Special focus is placed on the combination of assonant, or even alliterating, verbs and their direct objects. In order to compose such a stylistic figure, it is often necessary to make compromises regarding the semantics, leading to combinations that are still understood, but less than ideal to convey the meaning they are intended for. Phonological requirements override meaning. The technique applied by the authors of the spells is to start from a central determining concept (e.g., some kind of evil), then to combine it with a second variable concept that is somehow compatible with the intended sense (e.g., any verb of destruction that has the appropriate phonological shape) and to fill the clause with any further elements required on syntactic grounds. Although there are parallels between the techniques of Luwian spell composition and those in use in Mesopotamia (expected degree of assonance, choice of paraphernalia on account of the phonological shape of their designations), the locus of the use of the relevant words is not the same. In Luwian spells, both words must co-occur in the very same clause uttered in front of the ritual client in order to unfold their effects.

Nasal transfer in Hmongic

Abstract Hmongic languages exhibit a single nasal coda -ŋ corresponding to three codas in Mienic: -m, n, -ŋ, which suggests that Hmongic languages have lost place distinctions in nasal codas. However, in some words, Hmongic syllables with a nasal coda -ŋ correspond to Mienic syllables with no nasal coda, which has been recognized as posing a problem in reconstructing Proto-Hmong-Mien. This paper provides an explanation for this exceptional correspondence by hypothesizing a phonological change in Proto-Hmongic and in later stages whereby nasality transfers from a syllable-initial nasal to a tautosyllabic coda. This paper also explores the Proto-Hmongic rime categories to which the relevant words would be expected to belong if the nasal transfer did not apply.

Modeling the Remote Associates Test as Retrievals from Semantic Memory

The Remote Associates Test (RAT) is a word association retrieval task that consists of a series of problems, each with three seemingly unrelated prompt words. The subject is asked to produce a single word that is related to all three prompt words. In this paper, we provide support for a theory in which the RAT assesses a person's ability to retrieve relevant word associations from long‐term memory. We present a computational model of humans solving the RAT and investigate how prior knowledge and memory retrieval mechanisms influence the model's ability to match human behavior. We expand prior modeling attempts by investigating multiple large knowledge bases and by creating a cognitive process model that uses long‐term memory spreading activation retrieval processes inspired by ACT‐R and implemented in Soar. We evaluate multiple model variants for their ability to model human problem difficulty, including the incorporation of noise and base‐level activation into memory retrieval. We conclude that the main factors affecting human difficulty are the existence of associations between prompt words and solutions, the relative strengths and directions of those associations compared to associations to other words, and the ability to perform multiple retrievals.

Intelligent Video Tag Recommendation System Improving Video Popularity

A successful marketing activity requires attracting high social popularity to their contents, since higher popularity usually indicates stronger influence, more fame and higher revenue. In this, we focus on the question of how to improve popularity of videos sharing on websites like YouTube in mobile computing environment. Obviously, composing high quality titles and tags is beneficial for viewers to discover videos of their interests and increase their tendency to watch more videos. However, it is not an easy task for uploaders,which is especially true since the screen is tight for most mobile devices. To this end, this proposes a novel hybrid method based on multi-modal content analysis that recommends keywords for video uploaders to compose titles and tags of their videos and then to gain higher popularity. The method generates candidate keywords by integrating techniques of textual semantic analysis of original tags and recognition of video content. On one hand, taking the original keywords of a video as input, the method obtains most relevant words from WordNet and related video titles gathered from the three top video sharing sites (YouTube, Yahoo Video, Bing Video). On the other hand, through recognizing video content with deep learning technology,the method extracts the entity name of video content as candidate keywords. Finally, a TFSIM algorithm is proposed to rank the candidate keywords and the most relevant keywords are recommended to uploaders for optimizing the titles and tags of their videos

Illustration of attention mechanism in LSTM algorithm for analyzing its performance

The advent of technologies and equipments in Information Technology and Communication has increased the use of smart phones and social media by all people irrespective of age and gender. Many times people find social media posts as the outlet for draining all their emotions. Depression is a common mental health problem that ranges from low mood and loss of interest to persistent feeling of sadness and self-destruction. Mental Health Experts are interested in analyzing the physical and social media behavior of patients to diagnose depression. Attention Mechanism imitates the human cognitive of considering only the most relevant data needed for a particular analysis. LSTM is a state-of-the-art Recurrent Neural Network architecture used for depression analysis. In this paper we are further improving the LSTM Algorithm by proposing an Attention Mechanism that makes the model robust and improved. This major role of our Attention Mechanism is identifying the Prime Words and Supporting words that are relevant for the analysis. Distance is calculated between non-prime words and prime words, weights are assigned based on this calculation, higher weight being given to most relevant words. Making use of Attention helps us to focus only on the relevant data and neglect the irrelevant data.

Individual and School Correlates of DIT-2 Scores Using a Multilevel Modeling and Data Mining Analysis

Moral reasoning was investigated with respect to individual characteristics (i.e., education level, political orientation and sex) and school-related (i.e., university/college) factors using multilevel modeling and data mining analysis. We used the multilevel modeling to detect school effects on moral reasoning as well as individual effects for 16,334 students representing 79 different higher education institutions across the U.S. The school-related factors, such as the racial composition, student–faculty ratio, average SAT score, institution type, institutions’ geographical region, frequencies of morally relevant words in college course catalog, college mission and value statements were collected through website searches. Data mining analysis was utilized to extract and calculate the frequencies of morally relevant words from the website content. There were significant effects for the individual characteristic of political orientation. Additionally, all school-related factors were significant. Only main effects were observed for some school-related factors (i.e., average SAT score, institution type, frequency of morally relevant words in mission statements, value statements and course catalogs). For other school-related factors (i.e., the region, student–faculty ratio and racial composition), main effects were also observed; however, these effects were particularly illuminating given their interactions with political orientation. Implications for educational communities are discussed.

Relevant Word Determination from Dynamic Text Network

Text network has been of research interest in the computational domain. A text network contains textual documents with dynamic content. The textual documents linguistically depend upon the grammar and use of words. We have proposed determining relevant words in dynamic content using dynamic complex network approaches. We considered dynamic news updates for exploring our analysis of text networks. This work proposes a complex network-based approach for determining the change of important words with dynamic content. We have suggested determining relevant words from dynamic content using degree centrality, closeness centrality and clustering coefficient. We have proposed Affiliation Factor for bond strength determination among the important words. We have considered an instance of live updates in the news for our approach. The news updates on a particular live incident are uploaded dynamically in a thread. We have introduced the Instance Factor for perceiving the relevance of the important words throughout the document, apart from considering the co-occurrence frequency. The approach is free of semantic analysis and, consequently, exempt depending on any corpus. The analytical results show that the varying important words can be efficiently determined from the dynamic content of the text network compared to the other approaches.

Affection Enhanced Relational Graph Attention Network for Sarcasm Detection

Sarcasm detection remains a challenge for numerous Natural Language Processing (NLP) tasks, such as sentiment classification or stance prediction. Existing sarcasm detection studies attempt to capture the subtle semantic incongruity patterns by using contextual information and graph information through Graph Convolutional Networks (GCN). However, direct application of dependence may inevitably introduce noisy information and inferiorly in modeling long-distance or disconnected words in the dependency tree. To better learn the sentiment inconsistencies between terms, we propose an Affection Enhanced Relational Graph Attention network (ARGAT) by jointly considering the affective information and the dependency information. Specifically, we use Relational Graph Attention Networks (RGAT) to integrate relation information guided by a trainable matrix of relation types and synchronously use GCNs to integrate affection information explicitly donated by affective adjacency matrixes. The employment of RGAT contributes to information interaction of structural relevant word pairs with a long distance. With the enhancement of affective information, the proposed model can capture complex forms of sarcastic expressions. Experimental results on six benchmark datasets show that our proposed approach outperforms state-of-the-art sarcasm detection methods. The best-improved results of accuracy and F1 are 4.19% and 4.33%, respectively.

The Impact of Covid-19 on Islamic Economics and Finance Industry: Text Analytics using R

Covid-19 not only has an impact on the financial industry but also generally affected on the world economy. In the financial industry, currently, Islamic finance is growing fast. Thus, this study aims to determine the development of research on the impact of Covid-19 on the economy and the Islamic finance industry in the world by analyzing a study of 47 documents from the 2020-2021 period on the Scopus database using text analysis using R-studio. This study provides an analysis of keywords that are often used in the topic and provides an overview of the analysis of the most relevant words, word treemaps, co-occurrence networks, thematic maps, and conceptual structures. The results of this study indicate that Covid-19 has harmed several sectors of the Islamic economy and finance, including Islamic banking, Islamic capital markets, Islamic microfinance institutions, and tourism. In addition, the results of this study also provide several recommendations for several industries that have been negatively affected by the Covid-19 pandemic.

Vocabulary and Moments of Change: Thucydides and Isocrates on the Rise and Fall of Athens and Sparta


 This paper presents the events classical authors deemed as key moments in the rise and fall of Athens and Sparta, and examines the vocabulary they used to describe change: its extent (momentary or long-lasting), nature (evolution, transition, reversal, destruction, renewal), and effects (positive or negative). The Persian Wars, the end of the Peloponnesian War, and the battles of Cnidus, Naxos, and Leuctra were frequently thought of as the causes of Athens’ and Sparta’s growth or collapse, prosperity or misfortune. Τhese events were not only key moments in the balance of power in interstate relations, but also part of a particular argumentation which exploited and presented the phenomenon of change in several ways and through varying narratives. The study of the vocabulary pertaining to the rise and fall of the two cities reveals, on the one hand, the different ways one can treat change and, on the other hand, that the relevant words can convey neutral, positive or negative connotations, depending especially on an author’s intentions when emphasising a particular event.

Exploring the Influence of French on the Pronunciation of the Letter h in English Words

In English learning, what are the reasons behind when to not pronounce the letter h and when to pronounce it in English words? These are the questions that English learners struggle with. In this essay, the author will investigate the influence of French on the change of English pronunciation of the letter h in English words through a chronological approach: Old English, Medieval English and Modern English. In this way, the author hopes to help English learners further understand the influence of French on the pronunciation of English words with the letter h, and from there to help English learners to memorize the pronunciation of the relevant words.

Is it you you’re looking for?

Abstract Previous evidence has implicated personal relevance as a predictive factor in lexical access. Westbury (2014) showed that personally relevant words were rated as having a higher subjective familiarity than words that were not personally relevant, suggesting that personally relevant words are processed more fluently than less personally relevant words. Here we extend this work by defining a measure of personal relevance that does not rely on human judgments but is rather derived from first-order co-occurrence of words with the first-person singular personal pronoun, I. We show that words estimated as most personally relevant are recognized more quickly, named faster, judged as more familiar, and used by infants earlier than words that are less personally relevant. Self-relevance is also a strong predictor of several measures that are usually measured only by human judgments or their computational estimates, such as subjective familiarity, age of acquisition, imageability, concreteness, and body-object interaction. We have made all self-relevance estimates (as well as the raw data and code from our experiments) available at https://osf.io/gdb6h/.

A user study with aspect‐based sentiment analysis for similarity of items in content‐based recommendations

AbstractMost studies on recommender systems focus on collaborative algorithm approaches over content‐based recommendation due to their better accuracy results. However, the advantage of the latter is that it is more effective and more transparent with user applications. This article proposes WordRecommender, an explainable content‐based algorithm that calculates similarity by semantic proximity. Its preprocessing step involves analyses of movie reviews to obtain aspects, defined as relevant words of high sentimental value. Recommendations are generated by a neighbourhood algorithm that calculates the similarity of films based on the semantic proximity of the aspects ordered by their emotional score. It can also consider a semantic comparison of metadata using the most related aspects from the recommended movie and one enjoyed by the user for the production of textual explanations. The accuracy of the algorithm was competitive with those of other baseline neighbourhood methods, and the semantic data of items can be the source of both representative information and reasoning in recommender systems.

PUNet: Novel and efficient deep neural network architecture for handwritten documents word spotting

Ancient scripts, documents and photos are valuable supports that opened a window to humanity’s past, its social and cultural treasure. However, due to the inevitable deterioration, seeking information in these documents remains a key challenge. Word Spotting remains an attractive alternative to Optical Character Recognition systems for information retrieval, especially in the case of old documents. The quantity of research work on this particular topic continues growing by extracting relevant words as an alternative method of recognizing text content. Here, we propose a new robust Deep Learning based framework for data exploration of ancient documents by applying Transfer Learning from the U-Net Network and using recent Pyramidal Histogram of Character Encryption: no special preprocessing or Data augmentation optimizers are required. Experimental results reveal its accuracy and high capacity to well classify words of various handwritten datasets.

Green energy: identifying development trends in society using Twitter data mining to make strategic decisions

This study analyzes Twitter’s contribution to green energy. More than 200,000 global tweets sent during 2020 containing the terms “green energy” OR “greenenergy” were analyzed. The tweets were captured by web scraping and processed using algorithms and techniques for the analysis of massive datasets from social networks. In particular, relationships between users (through mentions) were determined according to the Louvain multilevel algorithm to identify communities and analyze global (density and centralization) and node-level (centrality) metrics. Subsequently, the content of the conversation was subject to semantic analysis (co-occurrence of the most relevant words), hashtag analysis (frequency analysis), and sentiment analysis (using the VADER model). The results reveal nine main communities and their leaders, as well as three main topics of conversation and the emotional state of the digital discussion. The main communities revolve around politics, socioeconomic issues, and environmental activism, while the conversations, which have developed mostly in positive terms, focus on green energy sources and storage, being aligned with the main communities identified, i.e., on political, socioeconomic, and climate change issues. Although most of the conversations have been about socioeconomic issues, the presence of leading company accounts was minor. The main aim of this work is to take the first steps toward an innovative competitive intelligence methodology to study and determine trends within different scientific fields or technologies in society that will enable strategic decisions to be made.

Therapeutic use of serious games in mental health: scoping review.

There has been an increase in the development and application of serious games to support management of mental ill health, but their full impact is unclear. Evaluation of the current evidence of acceptability and effectiveness of serious games in improving mental health disorders. A PRISMA-guided scoping review was conducted, using a predefined criteria and a relevant word combination on three databases: EMBASE, Medline and PsycINFO. Each included study was examined for game format, study type, number of participants, basic demographics, disorder targeted, recruitment, setting, control conditions, duration and follow-up, study attrition, primary outcomes and their results. Each study was given a Grading of Recommendations, Assessment, Development and Evaluations rating for quality. Fourteen out of 513 studies met the inclusion criteria. The serious games focused on symptoms of anxiety (n = 4), attention-deficit hyperactivity disorder (n = 3), depression (n = 2), schizophrenia (n = 2), alcohol use disorder (n = 2) and bipolar disorder (n = 1). There were multiple significant outcomes favouring serious games across conditions covered in the review. Study quality varied, with studies rated high (n = 3), moderate (n = 6), low (n = 3) and very low (n = 2). The available evidence suggests that serious games could be an effective format for an intervention to reduce mental health symptoms and improve outcomes of individuals. Better designed studies would further develop confidence in this area. This is a potential vehicle of change to deliver some of the much-needed psychiatric support to both economically developed and developing regions in a resource-utilitarian manner. Partnerships between the gaming industry, researchers and health services may benefit patients.

The assessment of interprofessional practice in mental health nursing with ethnographic observation and social network analysis: a confirmatory and bibliometric network study using VOSviewer.

Despite numerous techniques for assessing mental health nursing abilities and accomplishments, most practice in psychiatric wards is based on observable clinical behaviours and actions. VOSviewer can perform bibliographic network analysis (BNA), extracting all central topics that identify core behavioural skills in mental health nursing and essential elements in interprofessional practice (IPP). The current study captures the critical concepts in mental health nursing assessment by performing a BNA of essential topics on ethnography, social network analysis, and interprofessional care. A qualitative BNA with a VOSviewer extracted relevant topics from a total of 542 articles obtained from Microsoft API. A subsequent confirmatory quantitative analysis with NVIVO weighed the percentages of the relevant issues and words extracted by the VOSviewer. Boolean keywords searched were 'ethnography,' 'social network analysis,' 'interprofessional', 'psychiatry' and 'hospital'. Major themes identified in ethnography, IPP, and social network analysis for nursing assessment were those of 'communication' (11.63%), 'whole' (9.29%), 'knowledge' (7.66%), 'person' (7.52%), 'activity' (6.31%) and 'collaboration' (6.10%). The current study has proven the value of BNA in extracting relevant topics in target literature. VOSviewer captured salient issues in mental health nursing assessment, including ethnographic observations, social network analysis, and IPP. The results confirmed the value of focusing on collaborative care, reciprocity, knowledge management, and information sharing in assessing mental health nursing performances.