Psychoanalytic and Psychodynamic Practitioners Survey

There has been little consensus in the field of psychology in what defines a psychoanalytic/psychodynamic (PA/PD) practitioner or psychologist. This dissertation analyzed the data from the 2021 Psychoanalytic and Psychodynamic Practitioner’s Survey. The analyzed data was used to further understand who these practitioners are and how they practice by exploring (a) practice patterns, (b) education and training experiences, (c) demographics of practitioners, (e) practice settings and populations, (f), clinical problems addressed, and (g) needs and interest assessment for new specialty and subspecialty board certification. The results were analyzed and revealed relevant information about individuals’ ethnic/racial identification and the intersecting factors that influence populations and settings in which individuals practice. Additionally, data showed that that many PA/PD psychologists would be interested in board certification. The findings support the importance of having Board Certification for PA/PD psychologists and for continuing to understand how PA/PD practitioners’ practice. The implications of the findings for research, training, and practice are discussed

On the Use of Evaluation Measures for Defect Prediction Studies

Software defect prediction research has adopted various evaluation measures to assess the performance of prediction models. In this paper, we further stress on the importance of the choice of appropriate measures in order to correctly assess strengths and weaknesses of a given defect prediction model, especially given that most of the defect prediction tasks suffer from data imbalance. Investigating 111 previous studies published between 2010 and 2020, we found out that over a half either use only one evaluation measure, which alone cannot express all the characteristics of model performance in presence of imbalanced data, or a set of binary measures which are prone to be biased when used to assess models especially when trained with imbalanced data. We also unveil the magnitude of the impact of assessing popular defect prediction models with several evaluation measures based, for the first time, on both statistical significance test and effect size analyses. Our results reveal that the evaluation measures produce a different ranking of the classification models in 82% and 85% of the cases studied according to the Wilcoxon statistical significance test and Â12 effect size, respectively. Further, we observe a very high rank disruption (between 64% to 92% on average) for each of the measures investigated. This signifies that, in the majority of the cases, a prediction technique that would be believed to be better than others when using a given evaluation measure becomes worse when using a different one. We conclude by providing some recommendations for the selection of appropriate evaluation measures based on factors which are specific to the problem at hand such as the class distribution of the training data, the way in which the model has been built and will be used. Moreover, we recommend to include in the set of evaluation measures, at least one able to capture the full picture of the confusion matrix, such as MCC. This will enable researchers to assess whether proposals made in previous work can be applied for purposes different than the ones they were originally intended for. Besides, we recommend to report, whenever possible, the raw con- fusion matrix to allow other researchers to compute any measure of interest thereby making it feasible to draw meaningful observations across different studies

On the Relationship Between Story Point and Development Effort in Agile Open-Source Software

Background: Previous work has provided some initial evidence that Story Point (SP) estimated by human-experts may not accurately reflect the effort needed to realise Agile software projects. / Aims: In this paper, we aim to shed further light on the relationship between SP and Agile software development effort to understand the extent to which human-estimated SP is a good indicator of user story development effort expressed in terms of time needed to realise it. / Method: To this end, we carry out a thorough empirical study involving a total of 37,440 unique user stories from 37 different open-source projects publicly available in the TAWOS dataset. For these user stories, we investigate the correlation between the issue development time (or its approximation when the actual time is not available) and the SP estimated by human-expert by using three widely-used correlation statistics (i.e., Pearson, Kendall and Spearman). Furthermore, we investigate SP estimations made by the human-experts in order to assess the extent to which they are consistent in their estimations throughout the project, i.e., we assess whether the development time of the issues is proportionate to the SP assigned to them. / Results: The average results across the three correlation measures reveal that the correlation between the human-expert estimated SP and the approximated development time is strong for only 7% of the projects investigated, and medium (58%) or low (35%) for the remaining ones. Similar results are obtained when the actual development time is considered. Our empirical study also reveals that the estimation made is often not consistent throughout the project and the human estimator tends to misestimate in 78% of the cases. / Conclusions: Our empirical results suggest that SP might not be an accurate indicator of open-source Agile software development effort expressed in terms of development time. The impact of its use as an indicator of effort should be explored in future work, for example as a cost-driver in automated effort estimation models or as the prediction target

MEG: Multi-objective Ensemble Generation for Software Defect Prediction

Background: Defect Prediction research aims at assisting software engineers in the early identification of software defect during the development process. A variety of automated approaches, ranging from traditional classification models to more sophisticated learning approaches, have been explored to this end. Among these, recent studies have proposed the use of ensemble prediction models (i.e., aggregation of multiple base classifiers) to build more robust defect prediction models. / Aims: In this paper, we introduce a novel approach based on multi-objective evolutionary search to automatically generate defect prediction ensembles. Our proposal is not only novel with respect to the more general area of evolutionary generation of ensembles, but it also advances the state-of-the-art in the use of ensemble in defect prediction. / Method: We assess the effectiveness of our approach, dubbed as Multi-objective Ensemble Generation (MEG), by empirically benchmarking it with respect to the most related proposals we found in the literature on defect prediction ensembles and on multi-objective evolutionary ensembles (which, to the best of our knowledge, had never been previously applied to tackle defect prediction). / Result: Our results show that MEG is able to generate ensembles which produce similar or more accurate predictions than those achieved by all the other approaches considered in 73% of the cases (with favourable large effect sizes in 80% of them). / Conclusions: MEG is not only able to generate ensembles that yield more accurate defect predictions with respect to the benchmarks considered, but it also does it automatically, thus relieving the engineers from the burden of manual design and experimentation

Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Replication Study

In the last decade, several studies have explored automated techniques to estimate the effort of agile software development. We perform a close replication and extension of a seminal work proposing the use of Deep Learning for Agile Effort Estimation (namely Deep-SE), which has set the state-of-the-art since. Specifically, we replicate three of the original research questions aiming at investigating the effectiveness of Deep-SE for both within-project and cross-project effort estimation. We benchmark Deep-SE against three baselines (i.e., Random, Mean and Median effort estimators) and a previously proposed method to estimate agile software project development effort (dubbed TF/IDF-SVM), as done in the original study. To this end, we use the data from the original study and an additional dataset of 31,960 issues mined from TAWOS, as using more data allows us to strengthen the confidence in the results, and to further mitigate external validity threats. The results of our replication show that Deep-SE outperforms the Median baseline estimator and TF/IDF-SVM in only very few cases with statistical significance (8/42 and 9/32 cases, respectively), thus confounding previous findings on the efficacy of Deep-SE. The two additional RQs revealed that neither augmenting the training set nor pre-training Deep-SE play lead to an improvement of its accuracy and convergence speed. These results suggest that using semantic similarity is not enough to differentiate user stories with respect to their story points; thus, future work has yet to explore and find new techniques and features that obtain accurate agile software development estimates

A Versatile Dataset of Agile Open Source Software Projects

Agile software development is nowadays a widely adopted practise in both open-source and industrial software projects. Agile teams typically heavily rely on issue management tools to document new issues and keep track of outstanding ones, in addition to storing their technical details, effort estimates, assignment to developers, and more. Previous work utilised the historical information stored in issue management systems for various purposes; however, when researchers make their empirical data public, it is usually relevant solely to the study’s objective. In this paper, we present a more holistic and versatile dataset containing a wealth of information on more than half a million issues from 44 open-source Agile software, making it well-suited to several research avenues, and cross-analyses therein, including effort estimation, issue prioritisation, issue assignment and many more. We make this data publicly available on GitHub to facilitate ease of use, maintenance, and extensibility

Impact of a malaria intervention package in schools on Plasmodium infection, anaemia and cognitive function in schoolchildren in Mali: a pragmatic cluster-randomised trial.

BACKGROUND: School-aged children are rarely targeted by malaria control programmes, yet the prevalence of Plasmodium infection in primary school children often exceeds that seen in younger children and could affect haemoglobin concentration and school performance. METHODS: A cluster-randomised trial was carried out in 80 primary schools in southern Mali to evaluate the impact of a school-based malaria intervention package. Intervention schools received two interventions sequentially: (1) teacher-led participatory malaria prevention education, combined with distribution of long-lasting insecticidal nets (LLINs), followed 7 months later at the end of the transmission season by (2) mass delivery of artesunate and sulfadoxine-pyrimethamine administered by teachers, termed intermittent parasite clearance in schools (IPCs). Control schools received LLINs as part of the national universal net distribution programme. The impact of the interventions on malaria and anaemia was evaluated over 20 months using cross-sectional surveys in a random subset of 38 schools(all classes), with a range of cognitive measures (sustained attention, visual search, numeracy, vocabulary and writing) assessed in a longitudinal cohort of children aged 9-12 years in all 80 schools. RESULTS: Delivery of a single round of IPCs was associated with dramatic reductions in malaria parasitaemia (OR 0.005, 95% CI 0.002 to 0.011, p<0.001) and gametocyte carriage (OR 0.02, 95% CI 0.00 to 0.17, p<0.001) in intervention compared with control schools. This effect was sustained for 6 months until the beginning of the next transmission season. IPCs was also associated with a significant decrease in anaemia (OR 0.56, 95% CI 0.40 to 0.78, p=0.001), and increase in sustained attention (difference +0.23, 95% CI 0.10 to 0.36, p<0.001). There was no evidence of impact on other cognitive measures. CONCLUSION: The combination of malaria prevention education, LLINs and IPCs can reduce anaemia and improve sustained attention of school children in areas of highly seasonal transmission. These findings highlight the impact of asymptomatic malaria infection on cognitive performance in schoolchildren and the benefit of IPCs in reducing this burden. Additionally, malaria control in schools can help diminish the infectious reservoir that sustains Plasmodium transmission

Comparison of Efficacy and Ocular Surface Disease Index Score between Bimatoprost, Latanoprost, Travoprost, and Tafluprost in Glaucoma Patients

Aim. The purpose of this study is to evaluate and compare the efficacy of 4 prostaglandin analogues (PGAs) and to determine the incidence of ocular surface disease in newly diagnosed, primary open-angle glaucoma (POAG) patients started on one of those 4 PGAs: bimatoprost (benzalkonium chloride, BAK, 0.3 mg/mL), latanoprost (BAK 0.2 mg/mL), travoprost (polyquad), and tafluprost (BAK-free). Patients and Methods. In this single-center, open-label trial, 32 patients newly diagnosed with POAG were randomly started on one of the four PGAs. All patients underwent a complete ophthalmological exam at presentation and at 1, 3, and 6 months of follow-up. Dry eye disease (DED) was assessed using the original Ocular Surface Disease Index (OSDI) questionnaire, in order to evaluate the impact of the drops on the quality of life of patients. Results. The mean age was 60.06 years ± 11.76. All four drugs equally and significantly reduced the intraocular pressure (IOP) with respect to the baseline IOP. There was a trend for a slightly greater reduction of IOP with bimatoprost, but the difference was not found to be statistically significant when compared to other PGAs. OSDI scores were significantly superior for travoprost (10.68 ± 5.73) compared to the other three drugs (p<0.05). Latanoprost caused the most significant eyelash growth and iris discoloration. Conjunctival hyperemia and superficial keratitis occurrence were similar in the four groups. Conclusion. All prostaglandin analogues equally and significantly reduce the IOP in patients with POAG. According to the results of the OSDI score, latanoprost seems to be the least tolerated among the four drugs

Determining the most significant changes on intergenerational communication and young people’s family planning and reproductive health outcomes: Qualitative evaluation of the Merci Mon Héros media campaign in Niger and Côte d’Ivoire

Merci Mon Héros (MMH) is a mixed media campaign that catalyzes young people as co-leaders to improve intergenerational communication and young people’s family planning (FP) and reproductive health (RH) outcomes. Breakthrough ACTION co-facilitated and codeveloped this youth-led campaign in francophone African countries while the Breakthrough RESEARCH project was tasked with evaluating the MMH campaign to determine the most significant changes in the communities exposed to the mixed media campaign in Niger and Côte d’Ivoire. Using the qualitative methodology of Most Significant Change, participants shared personal narratives during focus group discussions in each country. The diverse stories collected in both countries demonstrate how the MMH campaign can create an enabling environment for young people and adults to begin communicating about FP/RH and access the information, support, and services they need. While there is still a long way to go to eradicate taboos around FP/RH for adolescents and dispel myths that access to information promotes promiscuity, this evaluation found that some youth and adults exposed to the MMH campaign are contributing to a more enabling environment for others around them to talk about and access FP/RH information and quality services