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The efficacy of interpersonal therapy as a psychotherapeutic intervention in the treatment of postpartum depression
No full textPostpartum Depression (PPD) is a serious depressive disorder that negatively affects some
women, challenging their sense of identity in their newly altered reality, leaving them
distraught and anxious, while they care for their new baby. PPD is a common but
incapacitating condition that can, in some cases, be life-threatening. PPD has a profound
impact on the patient, her baby, the mother-infant relationship and has further implications
for her partner and wider family. PPD can be diagnosed if the patient is capable of disclosing
how she is really feeling to her clinician. PPD is however, often undiagnosed and untreated.
Biological and psychosocial risk factors for PPD have been identified in recent studies.
Treatment for PPD is dependent on the severity of its symptoms and the patient’s ability to
function. PPD is a treatable disorder.
This dissertation examined Interpersonal Therapy as a treatment for PPD. This study
explored IPT as a psychotherapy that presents the patient with a biopsychosocial model, as a
way of understanding her situation. Findings endorse an emphasis on interpersonal
dysfunction and conflict resolution in the present, making it a practical therapy, linking
triggers to the patient’s mood. This theoretical research has attempted to evaluate
Interpersonal Therapy as a treatment for women with PPD. Findings of this study have
revealed that IPT is an effective, but relatively new method of treatment for PPD. IPT is still
in its infancy and has moved from clinical research to clinical practice in numerous countries
around the world, including Ireland.
This research has concluded therefore, that IPT, as a new therapy, requires further research, if
it is to be evaluated comprehensively in Ireland.
As a major public health issue, it is imperative that PPD is screened for, diagnosed and
treated, to allow new mothers a more positive experience with their new babies
Resilience, social support and self-esteem: Coping resources required for transition following job loss in midlife
No full textTo understand the importance of the variable’s resilience, social support and self-esteem for individuals aged 50 to 71+ who experienced job loss through redundancy or retirement was the aim of this study. Longevity is a phenomenon of our time as well as changing work environments and roles. Participants (n 121) were recruited through snowball sampling from an on-line quantitative questionnaire. It was hypothesised that variables were different for gender and the reason for job loss, no significance was found. Correlations were identified between Self-esteem and Resilience, and Social Support. Additional analysis found participants within 50-54 age group had lower self-esteem than participants in older age groups. Socio-economic and education status influence self-esteem and should be included in future research, as well as personality traits. In conclusion, as midlife is a time of psychosocial development, individuals who experience job loss should receive psychological support as part of their exit planning process
An investigation into the best practices of retaining millennials in the financial sectors
No full textThis research investigates the best practices of retaining millennial generation employees
within the financial sector. This study provided an understanding of the perspectives of
millennials in relation to the reasons for leaving previous roles. This study also outlined what
draws Millennials to staying with employers and their opportunities to grow professionally in
the workplace. This was studied in line with typical professional development opportunities
provided by management including feedback, appraisals, coaching training and
development and rewards. These perspectives uniquely assisted the analysis of best
practices in retaining Millennial employees within the financial sector, during the unusual and
challenging time of the global pandemic. A meta-analysis was carried out by comparing the
primary data with the secondary data gathered in this study. The primary data was collected
through quantitative research, using surveys as the data collection instrument and the
population sample included Millennial staff from five Allied Irish Bank branches across south
Dublin. This research found that high employee turnover is an evident issue in the financial
sector, that Millennials do require specific attention to retain them and that professional
development initiatives are surprisingly crucial in order to retain them. This brought together
valuable recommendations or a unique outline of best practices to solve the problem of high
Millennial turnover and low retention within the financial sector
Economic sanctions sanctions as a foreign policy instruments: operational issues for financial institutions caused by economic sanctions and anti-money regulation
No full textBackground- Economic sanctions are imposed globally to control over unethical and wrongdoings. However, complications in sanctions compliance is the big issue that impacts on the borrowing ability of individuals and the operational efficiency of the companies. This study is particularly focused on the financial service sector to explore the impact of economic sanctions and operations issues to the companies.
Aim and Objectives- This study is aimed to reveal the effectiveness of imposes economic sanctions for policy and security concerns. Additionally, this research more focuses on the operational issues for financial institutions caused by economic sanctions and anti-money regulation.
Research Method- Data was collected through the interview method (qualitative information) and secondary studies review (both qualitative and quantitative information). The total of 5 AML analysts/Compliance officers will be interviewed for the data collection based on surveying, and purposive sampling was used to select the respondents. Secondary data in the context of the knowledge of economic sanctions for the financial services explored and further evaluated in the context of the operational issues. Data analysing through thematic analysis method was conducted to address the way, and the extent economic sanctions impacted the financial service companies’ operational efficiency.
Research Questions- Main research question addressed in this dissertation to explore new and valuable knowledge of economic sanctions that is “What is the impact of sanctions and anti-money laundering provisions on the operations in the financial services industry?”
Findings and Conclusion- Qualitative results in support of the secondary data has been demonstrated that the economic sanctions imposition and compliance is the big challenge for the
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financial or banking institutions. The issues faced by the financial institutions in managing compliance with different and multiple sanctions; include intense consumption of resources along with time and costs. The wider list of sanctions imposed on the financial services, has absorbed staff efforts and time, along with and senior management time. Also, it has been raised operational costs in terms of training to the staff as per the frequent revisions and compliance with the multiple sanctions imposing in the different states.
Keywords- Economic Sanctions, AML, Operational Issues, Financial Services or Financial Service Operation
Classification of retinal pathology from OCT images using a parametric tuned CNN
No full textOptometrists nowadays use optical biopsy to get cross sectional images of the retina infected by
pathologies. This is also known as Optical Coherence Tomography (OCT). It is important to
identify the retinal diseases at an early stage to prevent damage to the vision. There is a lot of
research to be done to find a suitable method which can automatically detect retinal diseases.
Therefore, we propose this research for automatic detection of retinal diseases by using a novel
method of hyperparameter tuning instead of manually detecting the parameters of our
Convolutional Neural Network (CNN). The Model is tested on metrics such as F1-score,
precision, specificity, sensitivity, loss graph and accuracy. We also compare it with pretrained
state-of-the-art model of Inception V3 and result shows that hyperparameter-tuned CNN gets
better results. Being reliable, this proposed model can be used by optometrists to detect retinal
disorders at an early stage
The role of social media marketing and dark social in enhancing brand trust and attracting international students to Irish universities
No full textThe international higher education sector has become a big business due to increased student mobility. Strict immigration policies and other political uncertainty in the UK and the US have opened new opportunities for other, less recognized higher education destinations to recruit more international students. Ireland, an English-speaking country with relatively relaxed and inclusive immigration policy, has largely benefitted from this.
Universities are investing a lot of resources in marketing. Social media offers universities an excellent opportunity to reach and connect and influence the decision of potential students across borders. While businesses are consciously trying to measure the return on investment from their marketing spends, most of them overlook the traffic recorded by private messaging applications, also known as dark social, which restricts them from getting critical insights regarding the success of a marketing campaign.
This study was therefore undertaken to understand the role of social media marketing and dark social in the increasing number of international students coming to Ireland. This study used a sequential exploratory mixed method design for data collection and analysis. Qualitative data was collected through semi-structured interviews with marketing representatives of four Irish higher education institutes. A survey undertaken by 110 international students from different universities in Ireland was used to collect quantitative data.
The findings indicated that Irish universities relied on their agent network for raising awareness and used social media as second fiddle for providing detailed information to international students. Electronic word-of-mouth over private channels known as dark social also has had a significant role in the increase of international students coming to Ireland. After analysing the role of social media marketing and dark social in attracting international students to Irish universities, this thesis also provides recommendations for Irish universities to further exploit social media marketing for better student recruitment by overcoming the challenges, they faced
The effectiveness of education and contact at decreasing stigma and increasing help seeking surrounding bipolar disorder
No full textThe current study primarily investigates the effect of education and contact on stigma and help-seeking intentions surrounding bipolar disorder. A large body of research has identified education and contact as effective at decreasing mental health stigma, however research into their ability to decrease stigma and subsequently increase help-seeking surrounding bipolar disorder is limited. The study also aimed to determine whether attitudes predict help-seeking intentions, with past research identifying attitudes as important predictors of help-seeking. Three conditions (education n58, contact n50, control n57) were randomly assigned to n165 (male n74, female n91) participants who were members of the public recruited via a between groups true-experimental online survey design on social media platforms including Linked In, Facebook and Instagram. The study also incorporated a correlational aspect to determine if attitudes predict help-seeking. Two scale questionnaires, the Community Attitudes Towards Mental Illness and The General Help-Seeking Questionnaire assessed attitudes towards MI and help-seeking intentions respectively. Initial hypothesis suggested a decrease in stigma and increase in help-seeking intentions post conditions and that attitudes would predict help-seeking intentions. Findings demonstrated a non-significant decrease in stigma and increase in help-seeking intentions regardless of the condition applied and that attitudes did not predict help-seeking. The results of this study therefore refute general literature on mental health stigma that education and contact reduce stigma and that attitudes predict help-seeking. Future research should incorporate implicit measures of stigma and test interventions of a more educationally diverse population
Business to consumers (B2C): the effect of machine learning application in telecom customer churn management
No full textCustomer churn also know as customer attrition is one of the major challenges faced by telecoms service providers and other types of businesses. Revenue is lost annually and marketing budget is wasted due to customer attrition. In order to maintain a strong business to consumer management, companies adopt business intelligence and data analytic models to extract and process necessary customer information. The project research will be divided into two (a) to predict customer churn (b) to create innovative idea to maximize profit for telecom in business to customer sector. Two different analytical tools were used to process a public telecom dataset and model algorithms for classification. The aim of this project will suggest how to reduce losses in marketing cost, fraud, and create an innovative digital idea to breach the revenue gap between telecom and digital platforms using customer and network data for profit maximization
Working from home or shirking from home? Personality trait as a predictor of remote working preference
No full textThe positives and negatives of remote working have been hotly debated in recent years. Remote work has been hampered with scepticism around performance and trust. The sudden onset Covid-19 has made remote working a reality for many employees worldwide. The long term effects of this on employee mental health are still not known. Personality traits have historically been an accurate predictor of working behaviours and mental health. The primary aim of this study was to look for a relationship between personality traits and remote working preference. 258 participants took part in a remote working survey and personality trait test. The groups comprised of participants who worked remotely prior to Covid-19, worked remotely as a result of Covid-19 and who did not work remotely at all. The groups were compared on their personality trait scores and their remote working preference. Parametric and non-parametric statistical analyses were used to compare the means of each group against personality trait scores. The study found a significant relationship between two of the Big 5 personality traits and remote working preference. The traits were Emotional stability and Conscientiousness. Gender was also compared for remote working preference, no significant relationship was found. The secondary aim of this study was to initiate a test and learn process for a Remote Working Suitability Scale developed by the researcher, the R.W.S.S. The research has shown that personality traits should be taken into account when designing remote working policy and evidence-based mental health interventions
Comparison between KERAS library and FAST.AI library using convolution neural network(image classification) model
No full textIn past, many Data Scientists did research and developed model for Image Classification using Machine Learning and Deep Neural Network algorithms and through their it was found out Convolution Neural Network was and still the best Deep Neural Network Layer model for Image Segmentation, Image Classification and Object Detection. And to build CNN Model for image classification they used many Libraries such as KERAS, TENSORFLOW, SKLEARN, CAFFE, THEANO, NILEARN, FAST.AI and many more. Out of these libraries have choose only two libraries that has very good support of additional API Framework like KERAS uses TENSORBOARD which is the most used API Framework for Visualization and developing applications. Another library is FAST.AI which is uses PyTorch as support for specifically developing applications such as Computer Vision and Natural Language Processing etc. Both libraries have their own advantages, where KERAS has support of the world’s most used and high-level TENSORFLOW API Framework to run smooth on CPU and GPU. On other side FAST.AI uses pre-trained models and using them a basic image classification can be developed within few lines of codes which makes the coder work easy. To find out which library is best for Image Classification using Convolution Neural Network I developed this practical way to compare and find the answers. This project is implemented in a way, where the comparison can be done basis on the similar architecture, dataset, default Hyperparameters values, Tune Hyperparameter values, Epochs and Learning Rate. Both the models are built and have been effectively trained on 87000 images of American Sign Language dataset