Background Information Research Articles

Students’ Perspective on Undergraduate Research Experiences in Indonesian Dental Schools

Undergraduate (UG) research is considered as an essential part of dental education. Numerous dental schools have included required course-based undergraduate research in their curricula. However, the implementation of UG research courses in the curriculum may vary between dental schools. In the present study, we aimed to evaluate student perspectives on UG research in the curriculum of Indonesian dental schools. A total of 203 participants from 10 dental schools returned the questionnaire. The participants were clinical students of the dentistry profession program who completed their undergraduate dentistry program from 2017 to 2022. The majority of study participants favored UG research in the curriculum of the undergraduate dentistry study program. Less than 20% participants perceived UG research experiments were not important in dental education. Factors that influenced these perceptions included the availability of adequate time to complete the course and sufficient support from research supervisors. Recommendations for improvement included providing an adequate time to complete UG research and adequate supervision to guide students to understand the conceptual background information of the research topics, designs, and scientific communication of data interpretation. Regular monitoring of students’ performance and progress would ensure completion of UG research courses in a timely manner. In conclusion, although UG research as a compulsory course in the Indonesian dental curriculum was well received by the students, overcoming the challenges is essential for the improvement of the research environment for undergraduate dental students.

Knowledge-Enriched Prompt for Low-Resource Named Entity Recognition

Named Entity Recognition (NER) in low-resource settings aims to identify and categorize entities in a sentence with limited labeled data. Although prompt-based methods have succeeded in low-resource perspectives, challenges persist in effectively harnessing information and optimizing computational efficiency. In this work, we present a novel prompt-based method to enhance low-resource NER without exhaustive template tuning. First, we construct knowledge-enriched prompts by integrating representative entities and background information to provide informative supervision tailored to each entity type. Then, we introduce an efficient reverse generative framework inspired by question answering (QA), which avoids redundant computations. Finally, we reduce costs by generating entities from their types while retaining model reasoning capacity. Experiment results demonstrate that our method outperforms other baselines on three datasets under few-shot settings.

Perspectives on low-value care and barriers to de-implementation among primary care physicians: a multinational survey

BackgroundHealthcare costs are rising worldwide. At the same time, a considerable proportion of care does not benefit or may even be harmful to patients. We aimed to explore attitudes towards low-value care and identify the most important barriers to the de-implementation of low-value care use in primary care in high-income countries.MethodsBetween May and June 2022, we email surveyed primary care physicians in six high-income countries (Austria, Finland, Greece, Italy, Japan, and Sweden). Physician respondents were eligible if they had worked in primary care during the previous 24 months. The survey included four sections with categorized questions on (1) background information, (2) familiarity with Choosing Wisely recommendations, (3) attitudes towards overdiagnosis and overtreatment, and (4) barriers to de-implementation, as well as a section with open-ended questions on interventions and possible facilitators for de-implementation. We used descriptive statistics to present the results.ResultsOf the 16,935 primary care physicians, 1,731 answered (response rate 10.2%), 1,505 had worked in primary care practice in the last 24 months and were included in the analysis. Of the respondents, 53% had read Choosing Wisely recommendations. Of the respondents, 52% perceived overdiagnosis and 50% overtreatment as at least a problem to some extent in their own practice. Corresponding figures were 85% and 81% when they were asked regarding their country’s healthcare. Respondents considered patient expectations (85% answered either moderate or major importance), patient’s requests for treatments and tests (83%), fear of medical error (81%), workload/lack of time (81%), and fear of underdiagnosis or undertreatment (79%) as the most important barriers for de-implementation. Attitudes and perceptions of barriers differed significantly between countries.ConclusionsMore than 80% of primary care physicians consider overtreatment and overdiagnosis as a problem in their country’s healthcare but fewer (around 50%) in their own practice. Lack of time, fear of error, and patient pressures are common barriers to de-implementation in high-income countries and should be acknowledged when planning future healthcare. Due to the wide variety of barriers to de-implementation and differences in their importance in different contexts, understanding local barriers is crucial when planning de-implementation strategies.

Increased screen time and its association to migraine and tension-type headache: a cross-sectional investigation among Bangladeshi students

BackgroundIncreasing prevalence of screens among young people is a notable characteristic of the modern digital era. The study aimed to explore the prevalence and associated factors of migraine headache (MH)...

TA-YOLO: a lightweight small object detection model based on multi-dimensional trans-attention module for remote sensing images

AbstractObject detection plays a vital role in remote sensing applications. Although object detection has achieved proud results in natural images, these methods are difficult to be directly applied to remote sensing images. Remote sensing images often have complex backgrounds and small objects, which results in a highly unbalanced distribution of foreground and complex background information. In order to solve the above problems, this paper proposes a multi-head channel and spatial trans-attention (MCSTA) module, which performs remote pixel interaction from the channel and spatial dimensions respectively to complete the attention feature capture function. It is a plug-and-play module that can be easily embedded in any other natural image object detection convolutional neural network, making it quickly applicable to remote sensing images. First, in order to reduce computational complexity and improve feature richness, we use a special linear convolution to obtain three projection features instead of the simple matrix multiplication transformation in Transformer. Second, we obtain trans-attention maps in different dimensions in a manner similar to the self-attention mechanism to capture the interrelationships of features in channels and spaces. In this process, we use a multi-head mechanism to perform parallel operations to improve speed. Furthermore, in order to avoid large-scale matrix operations, we specially designed an attention blocking mode to reduce computer memory usage and increase operation speed. Finally, we embedded the trans-attention module into YOLOv8, added a new detection head and optimized the feature fusion method, thus designing a lightweight small object detection model named TA-YOLO for remote sensing images. It has fewer parameters than the benchmark model YOLOv8, and its mAP on the PASCAL VOC and VisDrone data sets increased by 1.3% and 6.2% respectively. The experimental results prove the powerful function of the trans-attention module and the excellent performance of TA-YOLO.

Assessing Saudi EFL Learners’ Use and Understanding of English Idioms: Perceptions, Problems, and Patterns of Learning

This study aims to investigate Saudi EFL learners’ perceptions of English idioms, their problems, and the patterns of their learning and understanding of English idioms. 458 students (around 10%) were selected through a random sampling method from the undergraduate learners in different levels of programs at the College of Business Administration and the College of Science & Humanities, Prince Sattam bin Abdulaziz University, Al-Kharj, KSA. As the study looked at how certain independent variables (gender, level/year of study, and parents’ profession) shaped dependent variables (perceptions/attitudes), a questionnaire was self-developed which includes modified items from existing research. A quantitative approach (descriptive statistics) was used to analyze the data. The findings revealed that Saudi EFL learners’ perceptions were very positive about English idioms and their impact on enhancing their English proficiency. It was found that lack of background information, unfamiliarity, variation in literal interpretation, lack of Arabic parallels, and insufficient EFL classroom training made learning English idioms challenging for respondents. It was also revealed that Saudi EFL learners acquired and comprehended English idioms via using them in sentences, consulting a dictionary, guessing from contexts, group discussions, and breaking them down into their constituent parts. One-way ANOVA analysis found no statistically significant difference among the respondents based on their demographic differences. The study holds implications for both teachers and students as it will help them teach and learn English idioms effectively. The most common method is translating English idioms into their language.

Differences in Exercise Stress, Job Satisfaction, Intention to Quit Exercise, and Quality of Life According to the Psychological Abuse Experiences of Elite Male Athletes

This study aimed to further understand psychological abuse in sports and contribute to the development of elite sports and athletes’ persistent performance by identifying the causal effects of psychological abuse on elite athletes’ exercise stress, job satisfaction, intention to quit exercise, and quality of life (QOL). Data were collected from 363 elite South Korean male athletes (ages ≥ 20 years) from August to September 2023. The independent variable for comparative analysis was the presence or absence of psychological abuse in elite male athletes by coaches. The participants were divided into two groups: a non-abuse-experienced group (Group 1) and an abuse-experienced group (Group 2). Participants’ demographic and athletic background information (e.g., career and sport) were also collected. This study showed that the three factors (exercise stress, intention to quit exercise, and QOL) were higher in Group 2 than in Group 1. These findings provide a meaningful analysis of the impact of psychological abuse on the mental health, persistence, and overall QOL of elite male athletes that can be used to develop countermeasures and policies against psychological abuse that threatens the mental health of elite athletes.

Update of the World Health Organization's Mental Health Gap Action Programme Guideline for Psychoses (Including Schizophrenia).

The World Health Organization's (WHOs) Mental Health Gap Action Programme (mhGAP) aims to improve healthcare for mental, neurological, and substance use disorders in nonspecialized settings, with a focus on low- and middle-income countries (LMICs). mhGAP includes guidelines for the treatment of psychoses (including schizophrenia), which were recently updated in 2023. The complexity of the WHO guideline update process and the updated recommendations on psychoses are presented. The WHO guideline development process is outlined as well as the evidence appraisal and the translation of the evidence into recommendations following the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. The guideline update process includes a review of the literature, a compilation of systematic reviews, and extracting data related to critical and important outcomes. The updated recommendations and the justifying evidence are discussed. The WHO mhGAP guidelines for psychoses are adapted to LMICs, and consist of 13 recommendations in 2023, whereof 5 were updated, and 1 recommendation was newly developed. Background information on how these recommendations were obtained, and significant changes since the previous guideline update in 2015 are provided. Unlike other guidelines, the WHO must consider various countries, contextual factors, and the WHO Model Lists of Essential Medicines when developing its guidelines. A transformation of the WHO guideline for psychoses into a living guideline would ensure always up-to-date recommendations and facilitate shared decision-making.

Abstract PO3-20-10: Case Series: Invasive Secretory Carcinoma

Abstract Secretory variant invasive ductal carcinoma is not well described in the literature but is defined by its unique histopathology. These tumors stain positive for S100 and are typically triple negative hormonal cancers with an overall good prognosis. There is speculation that secretory carcinoma in adults is more aggressive with a greater likelihood for tumor recurrence. We present two case studies of this rare histopathologic diagnosis. First, a 76-year-old female with a recurrent secretory variant, invasive ductal carcinoma of the left breast. Second, a 68-year-old male who presented with invasive and in situ secretory carcinoma. A thorough chart review of all available institutional and outside institution records was performed from initial diagnosis to current treatment. Background information and de-identified patient information was summarized for this case series. This is a 76-year-old female who presented to her physician in 2011 with a palpable left breast and axillary mass. Her workup demonstrated ER+/PR+/HER2- invasive ductal carcinoma with lobular features of the left breast measuring 2.3 cm, along with a 1.5 cm malignant axillary lymph node. She underwent a left breast lumpectomy with axillary dissection. She underwent adjuvant whole breast radiation, axillary nodal radiation, and was placed on anastrozole therapy for 5 years. Seven years later a recurrence was noted in the superior aspect of the left breast. MRI guided biopsy revealed recurrent left breast cancer with pathology of invasive ductal carcinoma, secretory variant. She elected to undergo lumpectomy alone. Final pathology demonstrated invasive ductal carcinoma, secretory variant, ER negative (0%), PR negative (0%), HER2 negative (0%), pT1bNx. She underwent accelerated adjuvant partial breast radiation. Follow up and surveillance with bilateral breast MRI two years later demonstrated a new left breast mass, found to be triple negative, recurrent secretory variant invasive ductal carcinoma. She underwent definitive treatment with a left skin sparing mastectomy, right sentinel lymph node biopsy, and a latissimus dorsi flap with implant placement. Final pathology demonstrated multifocal recurrent secretory carcinoma, two foci measuring 6 and 11 mm. Our second patient was a 68-year-old male who initially presented to the clinic for evaluation of a ventral incisional hernia. During his initial assessment, a palpable retro-areolar nodule was noted on physical exam. Subsequent mammography demonstrated a hypoechoic mass with irregular margins measuring 2.8 x 2.3 x 1.7 cm along with increased vascularity and calcifications. An ultrasound guided biopsy demonstrated invasive secretory carcinoma. Initial pathologic testing of the tumor demonstrated ER positive (5%), PR positive (1%), and HER2 negative. The patient was taken to the operating room for a total mastectomy with sentinel lymph node biopsy. His postoperative pathology demonstrated negative margins, and invasive secretory carcinoma with lymph nodes negative for metastatic disease. Secretory variant of invasive ductal carcinoma is rare and is determined by its unique immunohistochemical features. We present two cases of secretory carcinoma at our institution. Data suggests that local recurrence is common in patients with secretory breast cancer, occurring in 33-44% of cases. Ki-67 index has been used as a prognostic indicator for metastases and recurrence of disease. Current treatment regimens are diverse; however, surgery is favored as the primary method. This report shows that although indolent, multifocality and recurrence is plausible. Given the limited data on this subset of carcinoma, more investigation is warranted to describe the effectiveness of adjuvant treatment options. Case 1 –insert figure image– Histopathologic examination at 200x shows invasive carcinoma with dense eosinophilic secretory material. By immunohistochemistry, the cells are negative for ER (0%) and PR (0%) with a HER2Neu score of 0, supporting the diagnosis of invasive secretory carcinoma. Case 2 –insert figure image– Histopathologic examination at 200x shows invasive carcinoma with dense eosinophilic secretory material. The tumor cells are positive for CK5/6 (patchy), CD117, ER (focal and weakly), and S100; negative for p63 and calponin, supporting the diagnosis of invasive secretory carcinoma. NTRK3 gene rearrangement was detected on fluorescence in situ hybridization (FISH). Citation Format: Lauren Geisel, Hemanth Venkatesh, Alyssa Obermiller, Quyen Chu, Jessica Gielow, Danielle Henry. Case Series: Invasive Secretory Carcinoma [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-20-10.

MSO‐DETR: Metric space optimization for few‐shot object detection

AbstractIn the metric‐based meta‐learning detection model, the distribution of training samples in the metric space has great influence on the detection performance, and this influence is usually ignored by traditional meta‐detectors. In addition, the design of metric space might be interfered with by the background noise of training samples. To tackle these issues, we propose a metric space optimisation method based on hyperbolic geometry attention and class‐agnostic activation maps. First, the geometric properties of hyperbolic spaces to establish a structured metric space are used. A variety of feature samples of different classes are embedded into the hyperbolic space with extremely low distortion. This metric space is more suitable for representing tree‐like structures between categories for image scene analysis. Meanwhile, a novel similarity measure function based on Poincaré distance is proposed to evaluate the distance of various types of objects in the feature space. In addition, the class‐agnostic activation maps (CCAMs) are employed to re‐calibrate the weight of foreground feature information and suppress background information. Finally, the decoder processes the high‐level feature information as the decoding of the query object and detects objects by predicting their locations and corresponding task encodings. Experimental evaluation is conducted on Pascal VOC and MS COCO datasets. The experiment results show that the effectiveness of the authors’ method surpasses the performance baseline of the excellent few‐shot detection models.

New use of an old discourse marker: The rise of prefacing answers to questions with so

This study investigates a previously unresearched use of the discourse marker so: prefacing answers to questions from an interlocutor, informally coined “backstory so” and found to signal that the answer necessitates background information, or more complexity or length than the asker assumes the questioner expects, a function known to be carried out by well. This investigation was motivated by (i) negative attitudes toward this use of so, describing the speakers with attributes like annoying, condescending, confusing, and wrong; (ii) layperson claims that it is new; and (iii) non-scholarly writings by linguists reporting controversy over whether answering questions with so is actually new or a Zwickian Recency Illusion. This paper draws on spoken data from the Corpus of Contemporary American English from 1990 to 2016, and presents findings of 774 target tokens by 544 unique speakers starting in 1992. Results of a logistic regression show a statistically significant increase in the rate of this form over that time. I tentatively suggest that previous constraints against the discourse marker so prefacing answers to mark added information seem to have undergone a rapid language shift, competing with the older use of well, and supporting the layperson intuitions of newness.

Temporal refinement network: Combining dynamic convolution and multi-scale information for fine-grained action recognition

Fine-grained action recognition is challenging due to the nearly identical context, limited background information, and less distinct inter-class differences compared to coarse-grained actions.Effectively capturing spatio-temporal information is crucial for fine-grained action recognition models. To address the limitations of coarse-grained models in describing spatio-temporal context, we propose a Temporal Refinement Block (TRB) as an efficient component for fine-grained action recognition. The TRB enables our model to effectively model underlying semantics and global dependencies by generating spatial–temporal kernels of different scales and performing fully connected operations within the temporal dimension. Our experiments demonstrate the effectiveness of TRB in learning latent semantics and global dependencies. To further enhance the framework's performance, we incorporate an enhanced spatio-temporal pyramidal network (TPN) that collects beat information and utilizes dilated convolutions to boost multi-scale features.We refer to the proposed framework as the Temporal Refinement Network, abbreviated as TRN.Our TRN achieves competitive performance on the FineGym and Diving48 benchmarks.

Most Known Citrus L. Species and Breast Cancer Relationship

The genus Citrus L. belongs to the Rutaceae family of flowering plants and shrubs. Citrus genus has valuable edible fruits such as oranges, lemons, grapefruits, pomelos, and limes. Australia, Melanesia, Southeast Asia, East Asia, and South Asia are the native habitats of the genus Citrus. Indigenous societies in these regions have long utilized and tamed various Citrus species. Citrus fruits and their secondary metabolites have been reported to be useful agents in numerous studies to possess anti-inflammatory, antioxidant, and anticancer properties. This review gives an overview of Citrus species and background information on the potential anticancer properties of the compounds identified, along with the related in vitro and in vivo research. Studies from the past have revealed a variety of biological functions that Citrus compounds can regulate, such as angiogenesis, apoptosis, metastasis, cell cycle regulation, and cell proliferation. These promising data call for more investigation into the chemopreventative activity of Citrus and phytoconstituents.

Linking Cardiac Psychology and Cardiovascular Medicine via Self-Determination Theory and Shared Decision-Making.

Despite considerable progress in recent years, research in cardiac psychology is not widely translated into routine practice by clinical cardiologists or clinical health psychologists. Self-determination theory (SDT), which addresses how basic psychological needs of autonomy, competence, and relatedness contribute to the internalization of motivation, may help bridge this research-practice gap through its application to shared decision-making (SDM). This narrative review discusses the following: (a) brief background information on SDT and SDM, (b) the application of SDT to health behavior change and cardiology interventions, and (c) how SDT and SDM may be merged using a dissemination and implementation (D&I) framework. We address barriers to implementing SDM in cardiology, how SDM and SDT address the need for respect of patient autonomy, and how SDT can enhance D&I of SDM interventions through its focus on autonomy, competence, and relatedness and its consideration of other constructs that facilitate the internalization of motivation.

Emotional Eating among College Students in Israel: A Study during Times of War.

Emotional eating, the act of consuming food to cope with negative emotions rather than responding to hunger cues, can lead to overeating in an attempt to regulate and alleviate these emotions. This study aimed to assess emotional eating among college students in Israel, specifically during times of war, which present unique and heightened stressors that accumulate on top. A total of 575 participants from the Ashkelon Academic College completed an online questionnaire examining background information, stress levels, and emotional eating symptoms. Our findings indicate that factors, such as being female, not having children, younger age, lower body satisfaction, higher BMI, and increased stress, are predictors of heightened emotional eating. These results highlight risk factors predisposing college students to engage in emotional eating. Developing targeted interventions, particularly campus-based programs to address emotional eating by promoting healthy coping strategies, a positive body image, and stress management skills is needed. In addition, raising awareness concerning emotional eating risks during challenging life transitions and distressing situations is necessary. The college leadership, led by the departments of Nutrition, Psychology, and Public Health, in collaboration with stakeholders in the Israeli Ministry of Health, must consider the mental effects of war on students and their involvement in emotional eating.

AI in knowledge sharing, which ethical challenges are raised in decision-making processes for organisations?

PurposeThis study aims to identify and assess the key ethical challenges associated with integrating artificial intelligence (AI) in knowledge-sharing (KS) practices and their implications for decision-making (DM) processes within organisations.Design/methodology/approachThe study employs a mixed-methods approach, beginning with a comprehensive literature review to extract background information on AI and KS and to identify potential ethical challenges. Subsequently, a confirmatory factor analysis (CFA) is conducted using data collected from individuals employed in business settings to validate the challenges identified in the literature and assess their impact on DM processes.FindingsThe findings reveal that challenges related to privacy and data protection, bias and fairness and transparency and explainability are particularly significant in DM. Moreover, challenges related to accountability and responsibility and the impact of AI on employment also show relatively high coefficients, highlighting their importance in the DM process. In contrast, challenges such as intellectual property and ownership, algorithmic manipulation and global governance and regulation are found to be less central to the DM process.Originality/valueThis research contributes to the ongoing discourse on the ethical challenges of AI in knowledge management (KM) and DM within organisations. By providing insights and recommendations for researchers, managers and policymakers, the study emphasises the need for a holistic and collaborative approach to harness the benefits of AI technologies whilst mitigating their associated risks.

Designing learning experiences using serious games: innovative village case study

The increasing interest in Learning Experience Design (LXD) has consolidated this field as a new way to transform educational practices. Thanks to its interdisciplinary nature which is mainly rooted in the close relation between human–computer interaction, user experience design and the learning sciences, LXD is a field that demands frameworks to design and develop products that can grow into services, to create interactive learning environments able to provide improvement-driven analytics and to guarantee a significant and satisfactory experience, designed to achieve learning outcomes. Innovative Village serious video game (IVVG) is a service-oriented product within an entrepreneurship and innovation system of platforms developed as an abilities-focused learning environment, and that builds a case study for LXD. This research aims to contribute to the consolidation of the emergent field of Learning Experience Design by providing a case around the Entrepreneurship and Innovation area from EAFIT University in Medellin, a learning system that comprises several service-oriented products; by being one of the products that constitute this system, and as a serious video game, Innovative Village has proven to be a key player in facilitating the learning outcomes and the knowledge integration that stem from the learning environment of the Interactive Design program, where the students’ learning experiences take place. First, related theoretical concepts and historical data will be analyzed to provide background information, then the case study will be addressed focusing on the materials, methods, and results. The study shows that the video game encourages collaborative behavior between players, as perceived by a significant proportion of participants. The research establishes a link between this perception and the role of creaticides in the game. Learning Experience Design (LXD) is about creating products that link the learning process with key competences. The game “Innovative Village” exemplifies this approach and provides insights into design, use, and required competencies. It also presents a framework for designing user-centered learning experiences that incorporate assessments to enhance the learning process. This framework is applicable from the early stages and can be tested in real learning environments.

Bi-directional information guidance network for UAV vehicle detection

AbstractUAV vehicle detection based on convolutional neural network exits a key problem of information imbalance of different feature layers. Shallow features have spatial information that is beneficial to localization, but lack semantic information. On the contrary, deep features have semantic information that is beneficial to classification, but lack spatial information. However, accurate classification and localization for UAV vehicle detection require both shallow spatial information and high semantic information. In our work, a bi-directional information guidance network (BDIG-Net) for UAV vehicle detection is proposed, which can ensure that each feature prediction layer has abundant mid-/low-level spatial information and high-level semantic information. There are two main parts in the BDIG-Net: shallow-level spatial information guidance part and deep-level semantic information guidance part. In the shallow-level guidance part, we design a feature transform module (FTM) to supply the mid-/low-level feature information, which can guide the BDIG-Net to enhance detailed and spatial features for deep features. Furthermore, we adopt a light-weight attention module (LAM) to reduce unnecessary shallow background information, making the network more focused on small-sized vehicles. In the deep-level guidance part, we use classical feature pyramid network to supply high-level semantic information, which can guide the BDIG-Net to enhance contextual information for shallow features. Meanwhile, we design a feature enhancement module (FEM) to suppress redundant features and improve the discriminability of vehicles. The proposed BDIG-Net can reduce the information imbalance. The experimental results show that the BDIG-Net can achieve accurate classification and localization for UAV vehicles and realize the real-time application requirements.

Influence of Marketing Capability on the Performance of Pharmaceutical Companies in Kenya

Purpose: The main objective of the study is to assess the effects of marketing capability on the performance of pharmaceutical companies in Kenya.
 Methodology: The population comprised the 415 pharmaceutical companies that are operating within the country. The study used stratified random sampling with sample size of 103 respondents. The study targeted both manufacturers and distributors. Primary and secondary data was collected using a questionnaire covering the five variables and background information. The data was entered in SPSS version 23.0 for further analysis, descriptive statistics was computed such that the study used mean, frequencies, percentages and inferential statistics to test the relationship between the study variables.
 Findings: The correlation analysis revealed that there was a positive and significant association between marketing capability and firm performance (r = 0.608, p = 0.000). Regression of coefficients results revealed that marketing capability and firm performance are positively and significantly related (β =0.758, p=0.000). This implies that a unit increase in marketing capability would lead to increase in firm performance by 0.758. This implies that marketing capability significantly and positively influences performance of pharmaceutical companies in Kenya. Through marketing strategies, a firm is able to arouse interest of a customer to their products. It is concluded that the pharmaceuticals in Kenya have employed various marketing strategies that necessitate extension of pharmaceutical market hence improving performance.
 Unique Contribution to Theory, Practice and Policy: The study recommended the pharmaceutical company’s management should embrace modern techniques of marketing to ensure they promote the sale of their products. The firms should prioritize customers’ feedback to adequately fulfil their orders. Pharmaceutical companies should also embrace uniqueness in branding and quality to form a unique selling point for the company to arouse interest of the customer in the market.

Assessment of adults with low literacy skills: a review of methods

The paper reviews the methods for assessing different components of reading skills in adults with reading difficulties, along with functional reading skills. We are particularly interested in the assessment methods available to researchers and practitioners, developed predominantly in the research context, and not available solely in English. We discuss the large-scale international study, PIAAC, as an example of a framework for such assessments. Furthermore, we cover the following types of assessment tools: (1) self-assessment questionnaires, probing into comprehension difficulties and reading habits; (2) measures of print exposure, such as author recognition tests, correlating with other reading-related skills; (3) measures of word recognition and decoding, including reading aloud of words and pseudowords, as well as silent lexical decision tasks; (4) fill-in-the-blank tasks and sentence reading tasks, measuring predominantly local comprehension, entangled with decoding skills; (5) comprehension of longer reading passages and texts, focusing on functional texts. We discuss comprehension types measured by tests, text types, answer formats, and the dependence problem, i.e., reading comprehension tests that can be solved correctly without reading. Finally, we tap into the new ideas emerging from the AI systems evaluation, e.g., using questions generated from news articles or Wikipedia or asked directly by search engines users. In the concluding section, we comment on the significance of incorporating background information, motivation, and self-efficacy into the assessment of adult literacy skills.