bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving Transparency in the Residency Application Process: Survey Study
Authors: Ulin Lindsey , Bernstein Simone A , Nunes Julio C , Gu Alex , Hammoud Maya M , Gold Jessica A , Mirza Kamran M 
Published: November 2023

Background
Increasing numbers of residency applications create challenges for applicants and residency programs to assess if they are a good fit during the residency application and match process. Applicants face limited or conflicting information as they assess programs, leading to overapplying. A holistic review of residency applications is considered a gold standard for programs, but the current volumes and associated time constraints leave programs relying on numerical filters, which do not predict success in residency. Applicants could benefit from increased transparency in the residency application process.

Objective
This study aims to determine the information applicants find most beneficial from residency programs when deciding where to apply, by type of medical school education background.

Methods
Match 2023 applicants voluntarily completed an anonymous survey through the Twitter and Instagram social media platforms. We asked the respondents to select 3 top factors from a multiple-choice list of what information they would like from residency programs to help determine if the characteristics of their application align with program values. We examined differences in helpful factors selected by medical school backgrounds using ANOVA.

Results
There were 4649 survey respondents. When responses were analyzed by United States-allopathic (US-MD), doctor of osteopathic medicine (DO), and international medical graduate (IMG) educational backgrounds, respondents chose different factors as most helpful: minimum United States Medical Licensing Examination (USMLE) or Comprehensive Osteopathic Medical Licensing Examination (COMLEX) Step 2 scores (565/3042, 18.57% US-MD; 485/3042, 15.9% DO; and 1992/3042, 65.48% IMG; P<.001), resident hometown region (281/1132, 24.82% US-MD; 189/1132, 16.7% DO; and 662/1132, 58.48% IMG; P=.02), resident medical school region (476/2179, 22% US-MD; 250/2179, 11.5% DO; and 1453/2179, 66.7% IMG; P=.002), and percent of residents or attendings underrepresented in medicine (417/1815, 22.98% US-MD; 158/1815, 8.71% DO; and 1240/1815, 68.32% IMG; P<.001).

Conclusions
When applying to residency programs, this study found that the factors that respondents consider most helpful from programs in deciding where to apply differ by educational background. Across all educational groups, respondents want transparency around standardized exam scores, geography, and the racial or ethnic backgrounds of residents and attendings.
Acceptance of Medical Artificial Intelligence in Skin Cancer Screening: Choice-Based Conjoint Survey
Authors: Jagemann Inga , Wensing Ole , Stegemann Manuel , Hirschfeld Gerrit 
Published: November 2023

Background
There is great interest in using artificial intelligence (AI) to screen for skin cancer. This is fueled by a rising incidence of skin cancer and an increasing scarcity of trained dermatologists. AI systems capable of identifying melanoma could save lives, enable immediate access to screenings, and reduce unnecessary care and health care costs. While such AI-based systems are useful from a public health perspective, past research has shown that individual patients are very hesitant about being examined by an AI system.

Objective
The aim of this study was two-fold: (1) to determine the relative importance of the provider (in-person physician, physician via teledermatology, AI, personalized AI), costs of screening (free, 10€, 25€, 40€; 1€=US $1.09), and waiting time (immediate, 1 day, 1 week, 4 weeks) as attributes contributing to patients’ choices of a particular mode of skin cancer screening; and (2) to investigate whether sociodemographic characteristics, especially age, were systematically related to participants’ individual choices.

Methods
A choice-based conjoint analysis was used to examine the acceptance of medical AI for a skin cancer screening from the patient’s perspective. Participants responded to 12 choice sets, each containing three screening variants, where each variant was described through the attributes of provider, costs, and waiting time. Furthermore, the impacts of sociodemographic characteristics (age, gender, income, job status, and educational background) on the choices were assessed.

Results
Among the 383 clicks on the survey link, a total of 126 (32.9%) respondents completed the online survey. The conjoint analysis showed that the three attributes had more or less equal importance in contributing to the participants’ choices, with provider being the most important attribute. Inspecting the individual part-worths of conjoint attributes showed that treatment by a physician was the most preferred modality, followed by electronic consultation with a physician and personalized AI; the lowest scores were found for the three AI levels. Concerning the relationship between sociodemographic characteristics and relative importance, only age showed a significant positive association to the importance of the attribute provider (r=0.21, P=.02), in which younger participants put less importance on the provider than older participants. All other correlations were not significant.

Conclusions
This study adds to the growing body of research using choice-based experiments to investigate the acceptance of AI in health contexts. Future studies are needed to explore the reasons why AI is accepted or rejected and whether sociodemographic characteristics are associated with this decision.
Effect of a Medication Adherence Mobile Phone App on Medically Underserved Patients with Chronic Illness: Preliminary Efficacy Study
Authors: Hartch Christa E , Dietrich Mary S , Stolldorf Deonni P 
Published: November 2023

Background
Medication adherence is vital in the treatment of patients with chronic illness who require long-term medication therapies to maintain optimal health. Medication adherence, a complex and widespread problem, has been difficult to solve. Additionally, lower-income, medically underserved communities have been found to have higher rates of inadequate adherence to oral medications. Even so, this population has been underrepresented in studies using mobile medication adherence app interventions. Federally qualified health centers provide care for medically underserved populations, defined as communities and populations where there is a demonstrable unmet need for health services. These centers have been reporting an increase in a more complex chronic disease population. Including medically underserved individuals in mobile health studies provides opportunities to support this disproportionately affected group, work toward reducing health disparities in access to health care, and understand barriers to mobile health uptake.

Objective
The aim of this preliminary efficacy study was to evaluate the effects and feasibility of a commercially available medication adherence app, Medisafe, in a medically underserved adult population with various chronic illnesses seeking care in a federally qualified health center.

Methods
Participants in this single-arm pre-post intervention preliminary efficacy study (N=10) completed a baseline survey, used the app for 2 weeks, and completed an end-of-study survey. The primary outcome measures were medication adherence and medication self-efficacy. Feedback on the use of the app was also gathered.

Results
A statistically significant median increase of 8 points on the self-efficacy for adherence to medications scale was observed (P=.03, Cohen d=0.69). Though not significant, the adherence to refills and medications scale demonstrated a median change of 2.5 points in the direction of increased medication adherence (P=.21, Cohen d=0.41). Feedback about the app was positive.

Conclusions
Use of the Medisafe app is a viable option to improve medication self-efficacy and medication adherence in medically underserved patients in an outpatient setting with a variety of chronic illnesses.
Kaiphal (Myrica nagi): A Botanical Origin Drug (Dawa) of Unani Medicine for Prevention and Treatment of Common Disorders
Authors: AHMAD KALEEM , Quamri Mohd Aleemuddin 
Published: November 2023

Myrica nagi is a celebrity medicinal plant that is distributed in sub-Himalayan regions and holds several pharmacological actions and therapeutic effects along with their economic usage. In Unani System of Medicine (USM), it is well known, as Kaiphal and used in the prevention and the management of several common disorders viz. Amraz-i-Riya (Respiratory disease), Amraz-i-Hazam (Gastrointestinal disease), Amraz-i-Aasab (Nervine disease), and Amraz-i-Bawl (genitourinary disease). Recent clinical studies evaluate that the Kaiphal consist of numerous phytoconstituents viz. tannins, flavonoids, phenolic acids, terpenes, glycosides, amino acids, steroids, and volatile oils. These phytoconstituents are responsible for varieties of pharmacological actions viz. Anti-inflammatory, Anti-Oxidant, Anti-Allergic, Anti-helminthic, Neuroprotective, Nephroprotective, Anti-hypertensive, Anticancer, and Anxiolytic. Conventionally, different parts of Kaiphal are used in asthma, allergic disorder, inflammatory diseases, neurological disorders, renal disease, and cancer. The physicians of USM described properties and therapeutics uses of Kaiphal but data on safety and efficacy are limited and also lack of attraction of researcher toward a preclinical and clinical trial. Therefore, this review updated the knowledge about pharmacological and therapeutic effects, ethnomedicinal uses, adverse effects, prevention and treatment of common disorders and also focused on future research in the prospective era.
Keyword: Kaiphal, Myrica nagi, USM, Phytoconstituents, Asthma
Konvergensi Televisi Dan Media Sosial Dalam Distribusi Konten Berita
Authors: Firmansyah Dwi , Anindita Anindita , Widyaningrum Alifiah Ghaniyyu 
Published: November 2023

Media sosial dengan cepat menjadi outlet stasiun televisi dalam menayangkan berita di luar program yang dimilikinya. Pengabungan platform yang sudah nyata dilakukan adalah penggabungan media sosial dengan media kontemporer televisi. Konvergensi televisi dan media sosial berpotensi mendukung masa depan ekonomi baru untuk jurnalisme. Tujuan penelitian ini untuk mengetahui konvergensi televisi dan media sosial dalam distribusi konten berita daerah di SCTV. Teori yang mendukung penelitian ini adalah konvergensi kontinum. Model ini berfungsi sebagai instrumen untuk mendefinisikan dan mengevaluasi tahapan proses konvergensi yang terjadi dalam suatu ruang berita, dan bukan untuk menilai keberhasilan suatu ruang berita. Lima tahap tersebut terdiri atas tahap cross promotion, cloning, coopetition, content sharing, full convergence.Studi kasus digunakan sebagai suatu penjelasan komprehensif yang berkaitan dengan berbagi aspek seseorang, suatu kelompok, suatu organisasi, suatu program atau suatu situasi kemasyarakatan yang diteliti, diupayakan dan ditelaah sedalam mungkin. Studi kasus fokus pada distribusi konten berita daerah pada kanal Liputan 6 di Youtube. Teknik pengumpulan data melalui wawancara mendalam dan obeservasi lingkungan terhadap sampel penelitian. Key informan adalah Manajer Pengembangan Konten Berita Daerah, Lita Hariyani, yang bertanggung jawab merancang konten dan mendistribusikan konten berita dari biro daerah di seluruh Indonesia untuk ditayangkan di televisi dan media sosial.
The Impact of Live-Streaming Shopping Characteristics on Behavioral Intention of Shoppers
Authors: Mohamad Roseli Nur Hazwani , Hasan Zuhairah , Sauid Muna Kameelah , Romaiha Noor Rafhati , Abdul Karim Zatul Himmah 
Published: November 2023

Currently, live-streaming shopping has become one of the popular approaches to conducting business. Businesses shift from traditional online shopping to live-streaming shopping approach because of the benefits that live-streaming shopping could offer. This paper aims to study the impact of live-streaming shopping characteristics on behavioral intention among shoppers. Researchers used literature analysis and a Stimulus-Organism-Response (S-O-R) framework as the methods of the study. The findings showed that all characteristics had a positive impact on the behavioral intention of shoppers. The limitation of the study was the researcher focused only on the Stimulus and Response of the framework; the Organism factor of the framework was omitted.
Green Employee Training—A Remedy for Environmental Behaviour: The Case of Public Universities in Kenya
Authors: Odhiambo Grace Mwamburi , Waiganjo Esther Wangithi , Simiyu Alice Nanjala 
Published: November 2023

The global ecosystem is fast deteriorating due to the devastating effects of climate change resulting from humanity’s behaviour towards the environment. Increasing resource scarcity, loss of biodiversity and global warming with disastrous effects have become prevalent. Scientists unanimously blame anthropogenic (human) actions for the crisis and claim that the power to mitigate and preserve the environment lies within human behaviour change. Organizations are under pressure to act since they contribute significantly to climate change through their operations, products and services. By examining the influence of green employee training (GET) on employee pro-environmental behaviour (EPEB), the study sought to investigate whether environmental training could influence eco-friendly behaviour in employees of public universities in Kenya. A measurement model based on the Green Five taxonomy was developed and employed for the study. The model measured an array of employee green behaviours: transforming, conserving, avoiding harm, influencing others and taking initiative. A list of 31 public chartered universities accredited by the Commission for University Education in Kenya constituted the sampling frame, where a sample of 123 respondents was drawn from three purposively selected universities. The study adopted a correlational research design and employed mixed research techniques, combining both quantitative and qualitative approaches. An online questionnaire was used to collect primary data from respondents. Data was analysed using both descriptive and inferential statistics. Correlational analysis revealed a significant, positive correlation between GET and EPEB, indicating a strong, positive linear relationship between the predictor and response variables. ANOVA results revealed that green employee training significantly and positively predicts employee pro-environmental behaviour.

Keywords: Climate Change, Green Employee Training, Green Five taxonomy, Employee Pro-Environmental Behaviour.
A new method of linear programming
Authors: Ольховский Н.А. , Соколинский Л.Б. 
Published: November 2023

В статье представлен новый итерационный метод линейного программирования, получивший название “метод поверхностного движения”. Данный метод строит на поверхности многогранника, ограничивающего допустимую область задачи линейного программирования, путь от начальной граничной точки до точки, в которой достигается оптимальное значение целевой функции. Вектор движения строится в направлении максимального увеличения/уменьшения значения целевой функции. Представлено формальное описание алгоритма, реализующего метод поверхностного движения. Доказана теорема сходимости. Описанный метод предполагает использование глубокой нейронной сети прямого распространения для определения направления движения по граням допустимого многогранника. Для этого строится многомер ный локальный образ задачи линейного программирования в точке текущего приближения, который подается на вход нейронной сети. Множество размеченных прецедентов, необходимое для обучения нейронной сети, может быть получено с помощью апекс-метода.
The article presents a new iterative method of linear programming, called the surface movement method. This method builds a path from the initial boundary point to the point at which the optimal value of the objective function is achieved on the surface of a polytope that restricts the feasible region of linear programming problem. The movement vector defines the direction of the maximum increase/decrease in the value of the objective function. A formal description of the algorithm implementing the surface movement method is presented. The convergence theorem is proved. The described method involves the use of a feed forward deep neural network to determine the direction of movement along the edges of a feasible polytope. To do this, a multidimensional local image of the linear programming problem is constructed at the point of the current approximation, which is fed to the input of the neural network. The set of labeled precedents necessary for training a neural network can be obtained using the apex method.
Makna Hidup dan Subjective Well-Being Mahasiswa
Authors: Victoriana Evany , Manurung Rosida Tiurma , Azizah Endeh , Teresa Mia , Gultom Zuster Alvin 
Published: November 2023

Subjective well-being, atau kebahagiaan, diperlukan oleh semua individu, termasuk mahasiswa. Tujuan penelitian ini adalah untuk mengkaji hubungan antara meaning in life dan subjective well-being pada mahasiswa. Responden penelitian ini adalah 261 mahasiswa universitas Y di kota Bandung. Metode penelitian yang digunakan dalam penelitian ini adalah metode kuantitatif dengan penggunaan kuesioner dalam pengambilan datanya. Kuesioner yang digunakan dalam penelitian ini adalah Meaning in Life Questionnaire (MLQ), Satisfaction with Life Scale (SWLS) dan PANAS (Positive Affect dan Negative Affect Schedule). Data penelitian dianalisis dengan menggunakan korelasi Pearson. Hasil pengolahan data menunjukkan terdapat korelasi positif antara meaning in life dan subjective well-being. Dalam penelitian ini juga diperoleh hasil bahwa aspek-aspek dalam meaning in life (presence of meaning in life dan searching for meaning in life) berkorelasi dengan aspek-aspek dalam subjective well-being (life satisfaction, positive affect, dan negative affect). Terkecuali, searching for meaning tidak berkorelasi dengan negative affect. Melalui penelitian ini disimpulkan bahwa semakin memiliki makna dalam kehidupan, maka mahasiswa akan semakin dapat mencapai subjective well-being yang tinggi, yang ditandai dengan dimilikinya kepuasan dalam kehidupan, didominasi afek positif dibandingkan afek negatif.
Implentasi Perangkat Lunak Attention Distraction Test
Authors: Hangkawidjaja Aan Darmawan , Ratnadewi Ratnadewi , Analya Priska , Adelina Ira 
Published: November 2023

Attention Distraction Test (ADT) adalah alat yang mengukur atensi. ADT berupa beberapa lampu berbentuk kotak yang dapat diatur mengenai berapa jumlah lampu yang akan menyala. Distraksi pada tes ini adalah adanya lampu lain di sekeliling lampu. Tujuan Penelitian ini adalah membuat implementasi perangkat lunak dari alat tes atensi dan distraksi. Hal ini penting dilakukan karena masih adanya keterbatasan pada ADT dalam variasi distraksi, dan cara mencatat jawaban. Perangkat lunak ADT melalui dua tahap, yang pertama yaitu pengujian untuk memastikan semua perangkat berfungsi dengan baik dan dapat merekam data dengan baik. Yang kedua, rancangan akan diujikan ke responden untuk menganalisa hasilnya. Apabila masih diperlukan penyempurnaan akan dilakukan perbaikan perancangan dan pembuatannya. Hasil menunjukkan bahwa alat ini dapat digunakan dengan baik untuk mengukur level atensi seseorang karena dapat mencatat waktu reaksi dan respon jawaban responden secara akurat. Apabila responden dapat menjawab dengan benar dan waktu reaksi semakin cepat maka dapat dikatakan bahwa responden tersebut memiliki level atensi yang lebih baik. Saran untuk penyempurnaan rancangan adalah dengan menambahkan data latar belakang pribadi dari responden, alternatif warna lain dari lampu distraksi dan melengkapi hasil rekapitulasi data dengan jumlah jawaban yang benar dan rata-rata dari waktu reaksi.
Factors Shaping Thai Millennials' Low-Carbon Behavior: Insights from Extended Theory of Planned Behavior
Authors: Thanapongporn Ajaree , Saengchote Kanis , Gowanit Chupun 
Published: November 2023

Objective: This research serves a dual purpose: To construct a predictive model for low-carbon behavior among Thai millennials and to analyze the interplay between socio-demographic variables and eco-conscious actions. Methods/Analysis: By employing PLS-SEM and surveying 150 Thai millennials through purposive sampling, this study reaffirms the influence of persuasive technology and incentives on low-carbon behaviors. It highlights the significance of perceived behavioral control within the TPB framework and reveals intricate pathways by which persuasive technology and incentives shape attitudes, perceived control, and social norms, thereby driving eco-friendly actions. Findings: Among Thai millennials, positive attitudes and perceived control drive low-carbon behavior, while social norms and accessible low-carbon infrastructure also impact eco-conscious actions. Persuasive technology shows promise for attitude adjustment, but incentives' relationship with low-carbon behavior is complex. Interestingly, mature women exhibit more low-carbon behavior, whereas education and income show an inverse relationship. Novelty/Improvement: This study contributes novel and substantial insights into the drivers of low-carbon behavior among Thai millennials by integrating diverse theoretical frameworks. It enriches our understanding of the mediating role of TPB factors and socio-demographic dimensions, offering invaluable guidance for stakeholders in crafting effective interventions while aligning seamlessly with Sustainable Development Goals 7, 9, 12, and 13. Doi: 10.28991/HIJ-2023-04-03-02 Full Text: PDF
Comparison of CNN Classification Model using Machine Learning with Bayesian Optimizer
Authors: Surono Sugiyarto , Afitian M. Yahya Firza , Setyawan Anggi , Eni Arofah Dyiyah Kresna , Thobirin Aris 
Published: November 2023

One of the best-known and frequently used areas of Deep Learning in image processing is the Convolutional Neural Network (CNN), which has architectural designs such as Inceptionv3, DenseNet201, Resnet50, and MobileNet used in image classification and pattern recognition. Furthermore, the CNN extracts feature from the image according to the designed architecture and performs classification through the fully connected layer, which executes the Machine Learning (ML) algorithm tasks. Examples of ML that are commonly used include Naive Bayes (NB), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Decision Tree (DT). This research was conducted based on an AI model development background and the need for a system to diagnose COVID-19 quickly and accurately. The aim was to classify the aforementioned CNN models with ML algorithms and compare the models’ accuracy before and after Bayesian optimization using CXR lung images with a total of 2000 data. Consequently, the CNN extracted 80% of the training data and 20% for testing, which was assigned to four different ML models for classification with the use of Bayesian optimization to ensure the best accuracy. It was observed that the best model classification was generated by the MobileNetV2-SVM structure with an accuracy of 93%. Therefore, the accuracy obtained using the SVM algorithm is higher than the other three ML algorithms. Doi: 10.28991/HIJ-2023-04-03-05 Full Text: PDF