The increased number of international immigrants and associated global problems of health deterioration and vitamin D (vitD) deficiency/insufficiency may lead to significant burdens for host countries. This thesis investigated immigrants’ health deterioration and vitD status through a comprehensive analysis of Canadian national vitD data, systematic evaluation of the quality/content of clinical practice guidelines, and global systematic review of vitD status and determinants among first-generation immigrants. Immigrants had lower serum 25-hydroxyvitamin D (S-25(OH)D) and higher melanin levels than non-immigrants. S-25(OH)D levels improved over time, with ethnicity the main factor explaining variations. The longer immigrants lived in Canada, the higher the prevalence of chronic diseases (CDs), potentially reflecting health deterioration. Low levels of accumulated S-25(OH)D may impact CD-related biomarkers, partially explaining immigrants’ health deterioration over time. Local and international guidance regarding immigrants’ vitD deficiency/insufficiency was lacking. Improving immigrants’ vitD status requires prevention and intervention programs (e.g., vitD supplementation/screening), relevant national/international guidelines, and longitudinal research clarifying the complex bidirectional association between S-25(OH)D and CDs.

Vid stor mängd data i form av kundomdömen kan det vara ett relativt tidskrävande arbeteatt bedöma varje omdömes sentiment manuellt, om det är positivt eller negativt laddat. Denna avhandling har utförts för att automatiskt kunna klassificera kundomdömen efter positiva eller negativa omdömen vilket hanterades med hjälp av maskininlärning. Tre olika djupa neurala nätverk testades och jämfördes med hjälp av två olika ramverk, TensorFlow och Keras, på både större och mindre datamängder. Även olika inbäddningsmetoder testades med de neurala nätverken. Den bästa kombination av neuralt nätverk, ramverk och inbäddningsmetod var ett Convolutional Neural Network (CNN) som använde ordinbäddningsmetoden Word2Vec, var skriven i ramverket Keras och gav en träffsäkerhetpå ca 88.87% med en avvikelse på ca 0.4%. CNN gav bäst resultat i alla olika tester framför de andra två neurala nätverken, Recurrent Neural Network (RNN) och Convolutional Recurrent Neural Network (CRNN) / With large amount of data in the form of customer reviews, it could be time consuming to manually go through each review and decide if its sentiment is positive or negative. This thesis have been done to automatically classify client reviews to determine if a review is positive or negative. This was dealt with by machine learning. Three different deep neural network was tested on greater and lesser datasets, and compared with the help of two different frameworks, TensorFlow and Keras. Different embedding methods were tested on the neural networks. The best combination of a neural network, a framework and anembedding was the Convolutional Neural Network (CNN) which used the word embedding method Word2Vec, was written in Keras framework and gave an accuracy of approximately 88.87% with a deviation of approximately 0.4%. CNN scored a better result in all of the tests in comparison with the two other neural networks, Recurrent NeuralNetwork (RNN) and Convolutional Recurrent Neural Network (CRNN).

OBJECTIVE: The purpose of this study was to determine the association between baseline patient recovery expectations and outcomes following physical therapy care. METHODS: PubMed, CINAHL Complete, PEDro, SPORTDiscus, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and PsycINFO were searched from inception to February 2021. Concepts represented in the search included physical therapy, patient expectations, patient outcomes, and their relevant synonyms. Two reviewers independently screened studies of article abstracts and full texts. Eligibility criteria included English language studies that evaluated adults seeking physical therapist intervention for any health condition where both patient outcome (recovery) expectations and functional or other outcome measures were reported. Methodologic standards were assessed using the Critical Appraisal Skills Program criteria. Data were extracted using a custom template for this review with planned descriptive reporting of results. Vote counting was used to measure reported outcomes. RESULTS: Twenty-one studies were included in this review, representing 4879 individuals. Studies were most commonly prospective cohort studies or secondary analyses of controlled trials. Varied expectation, outcome, and statistical measures that generally link patient recovery expectations with self-reported outcomes in musculoskeletal practice were used. CONCLUSION: Patient recovery expectations are commonly associated with patient outcomes in musculoskeletal physical therapy. IMPACT: Evidence supports measuring baseline patient expectations as part of a holistic examination process.

The author presents a case study for the development of an evidence synthesis service serving researchers outside of the health sciences at a large academic library. The purpose of this project was to highlight the importance of integrating existing core services and workflows that create seamless delivery of evidence synthesis support and yield quality service. The paper provides decisions that university libraries must consider as they are planning the implementation of these services. The research-intensive nature of evidence synthesis projects provides liaison librarians with a perfect entry to deliver high-quality and relevant services, especially to graduate students and researchers. Through this work, libraries will demonstrate strong evidence for their engagement and contributions to research on campus. Librarians interested in supporting this latest type of research can use this study to gain a sense of where their own professional strengths fit within this new functional area and plan how to enter this developing research space. This study may be of interest to administrators, reference and instruction librarians, and librarians serving graduate students. Issues are presented in a way that surfaces difficult topics, which will help guide planning conversations between supervisors and librarians regarding workload assignments.

Storytelling is one of the most creative, powerful and effective tools of modern marketing strategies and communication. Due to the expansion of social media, storytelling has become a popular marketing and sales tool that may affect consumer behavior. This research has been conducted from a storytelling perspective with the purpose of finding the correlation between consumer reviews and consumer purchasing behavior. This study focuses on consumer stories which are reflected in terms of product reviews and ratings. Where the correlation between these reviews and ratings and their effects on overall product sales are investigated. The study answers the question, “If reviews & rating have a correlation with consumer purchasing behavior in digital marketing, and if so then how this correlation could be defined?” The data collection and tests have been conducted in a practical environment. The German online stores have been used as the main mode of data collection. Depending on the nature of the data, the quantitative analysis approach has been adopted. By conducting real-time sales data tests, this study fills the gap in previous consumer research studies. The in-depth analysis of results show the correlation between consumer reviews and product sales. The results have revealed that consumer reviews may influence overall product sales and help a consumer in purchasing decision. / Storytelling är ett av de mest kreativa, kraftfulla och effektiva redskapen i modern marknadsföring. På grund av den ökande användningen av sociala medier har storytelling blivit ett populärt redskap för att påverka konsumenternas beteende. Denna studie har utförts från perspektivet av storytelling med syftet att finna en korrelation mellan kundrecensioner och kundernas köpbeteende. Studien fokuserar på kundernas historier som reflekteras i recensioner och betyg, där korrelationen mellan recensioner och betyg och övergripande försäljning undersöks. Studien syftar till att besvara frågan ”Om recensioner & betyg har en korrelation med kundernas köpbeteende i digital marknadsföring, och om så hur kan korrelationen definieras?” Datainsamlingen och testandet har utförts i en praktisk miljö. Tyska online butiker har använts som huvudsaklig metod för datainsamling. På grund av strukturen på data har en kvantitativ analys metod använts. Genom att genomföra tester i realtid, har denna studie kompletterat tidigare forskning kring kundbeteende. Den fördjupade analysen av resultaten visar att det finns en korrelation mellan kundrecensioner och försäljning. Resultaten har visat att kundrecensioner kan påverka försäljningen av produkter och hjälpa en konsument i ett köpbeslut.

Under det senaste decenniet har informationsteknologin utvecklats explosionartat. Införandet av smartphones och surfplattor som alltid är anslutna till Internet har förändrat vårt sätt att "konsumera" den. Allt detta ledde till digitaliseringen av betting tjänster och flytten till Internet. I deras tidiga dagar hade online betting tjänster ett lätt jobb att göra när det gällde positionering, de behövde bara finns på Internet och det var det som lockade kunder. Men tiderna har förändrats och online betting tjänster står inför nya utmaningar. De måste hitta en tydlig positionering och ha en konkurrensfördel gentemot sina konkurrenter, men det är inte en lätt uppgift. Därför koncentrerade sig denna studie på en utredning av positioneringen för dessa tjänster. Fallstudietillvägagångssätt valdes för detta examensarbete och det undersökta företaget var Betsson Group, som är ett av de större företagen med många varumärken i sin portfölj. Forskningen är begränsad till deras tre mest populära varumärken: Betsson, Betsafe och NordicBet med undantag för text mining där Bet365 (en konkurrent) också analyserades. Empirisk data som samlades in under denna studie kom från intervjuer på fallstudieföretaget,frågeformulär besvarat av kunder och genom text mining av online betting tjänsters omdömen. Studien resulterade i att hitta gapet mellan positioneringen av företaget, kunderna och online recensenterna. Kundernas vy och recensenters vy överrensstämmer ganska bra, men  öretagets uppfattning är helt avvikand. När kunderna väljer online betting tjänst bryr de sig mest om bästa odds, bonusar och mängden av betts, där å andra sidan företag placerar sig på ett statiskt sätt med målgrupper: folklig, nerdy och lyxig. Kunderna tenderar att inte bry sig om varumärkespositioneringen och driver företag in i ett priskrig, men samtidigt måste de känna igen varumärket för att lita på det med sina pengar. Studien bidrar till att förstå kunders vy när de väljer en online betting tjänst och denna  spekt kan generaliseras för hela online bettingindustrin. Dessutom kan en del av teorierna appliceras på andra online tjänster där bästa pris söks. / During the last decade, IT had a big burst. The introduction of smartphones and tablets that are always connected to the Internet changed our way of “consuming” it. All that lead to digitalization of betting services and moving them to the Internet. In their early days online betting services had an easy job to do regarding positioning, they only needed to exist on the Internet and that attracted customers. But, times have changed and online betting services face new challenges. They must find a clear positioning and have a competitive advantage over their competitors, but that is not an easy task to do. Therefore, this study concentrated on the investigation of the positioning for these services. Case study approach was chosen for this thesis and the investigated company was Betsson Group, which is one of the bigger companies with many brands in their portfolio. However, the research was delimited to their three most popular brands: Betsson, Betsafe and NordicBet with exception to text mining where Bet365 (a competitor) were also analyzed. Empirical data gather during this study came from interviews at the case company, questionnaire with customers and through text mining of online betting services reviews. The study resulted in finding the gap, in positioning, between the company, the customers and the online reviewers. Customers view and reviewers view align quite well, however company view is completely unaligned. When choosing an online betting service, customers mostly care about best odds, bonuses and variety of bets, where on the other hand companies position themselves in a static way with targets group of folksy, nerdy and luxurious. Customers tend to not care about the brand positioning, and push companies into a price war, however, they need to know the brand in order to trust it with their money. The study contributes to understanding the customers view when they choose an online betting service and this aspect can be generalized for the online betting industry as a whole. Moreover, some of the theories can be applied to other online services where best price is searched for.

The full text will be available at the end of the embargo: 15th Dec 2026

Background: Movies have become an important marketing and advertising tool that can influence consumer behaviour and trends. Reading film reviews is an im- important part of watching a movie, as it can help viewers gain a general under- standing of the film. And also, provide filmmakers with feedback on how their work is being received. Sentiment analysis is a method of determining whether a review has positive or negative sentiment, and this study investigates a machine learning method for classifying sentiment from film reviews. Objectives: This thesis aims to perform comparative sentiment analysis on textual IMDb movie reviews using lexicon-based and BERT neural network models. Later different performance evaluation metrics are used to identify the most effective learning model. Methods: This thesis employs a quantitative research technique, with data analysed using traditional machine learning. The labelled data set comes from an online website called Kaggle (https://www.kaggle.com/datasets), which contains movie review information. Algorithms like the lexicon-based approach and the BERT neural networks are trained using the chosen IMDb movie reviews data set. To discover which model performs the best at predicting the sentiment analysis, the constructed models will be assessed on the test set using evaluation metrics such as accuracy, precision, recall and F1 score. Results: From the conducted experimentation the BERT neural network model is the most efficient algorithm in classifying the IMDb movie reviews into positive and negative sentiments. This model achieved the highest accuracy score of 90.67% over the trained data set, followed by the BoW model with an accuracy of 79.15%, whereas the TF-IDF model has 78.98% accuracy. BERT model has the better precision and recall with 0.88 and 0.92 respectively, followed by both BoW and TF-IDF models. The BoW model has a precision and recall of 0.79 and the TF-IDF has a precision of 0.79 and a recall of 0.78. And also the BERT model has the highest F1 score of 0.88, followed by the BoW model having a F1 score of 0.79 whereas, TF-IDF has 0.78. Conclusions: Among the two models evaluated, the lexicon-based approach and the BERT transformer neural network, the BERT neural network is the most efficient, having a good performance score based on the measured performance criteria.

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