This article uses strategy metaphors consisting of a plan, a home and a game to study government programme formation in Finland. The strategy approach both contradicts and complements the traditional political science approach to government formation. The government programme has been strategic in the sense of separating the formulation and implementation parts of the strategy. The most important function of the metaphor of plan is to hold coalition parties together. The adopted austerity policy provides a meagre contribution to the expansion of services and the increase in government spending. Consequently, the home metaphor in the government programme appears in the distant future and in combating external threats. The game metaphor is apparent in the goal of making contracts with social partners. The vocabulary change from politics to strategy alters the government programme’s position in terms of catering to the needs of civil servants, citizens and stakeholders. The strategy perspective might be instrumental in shifting open democratic debates to closed and secretive policy formations.

The governance of public sector infrastructure projects became an important topic of interest in the project, programme and portfolio management literature during the last decade. Today, it is becoming a central focus for policymakers seeking to ensure success in selecting, designing and implementing government-sponsored programme of multi-projects. Due to the multiple underlying risks and complexities, the governance of infrastructure programme constitutes a critical element in strategic planning in developing countries. This paper has analyzed infrastructure development programme and revealed shortcomings in the areas of appraisal, decision-making, quality assurance and stakeholder management. Approaches to remedy these shortcomings have been proposed.

School Feeding Programmes are social safety net interventions providing educational and health benefits to vulnerable children in developing countries. This study assessed the impact of Osun Elementary School Feeding and Health Programme (O-MEALS) on the enrolment and retention of rural primary schools in Osun State. Multi stage sampling technique was used to select 188 respondents while data was collected through interview schedule. Percentages, Chi-square, PPMC and T-test were used for data analysis. Findings revealed that over half (51.6%) of the respondents were male, aged 9.38 ± 1.84 years and majority (83.0%) had a family size of 6-10 people. Food incentives (=1.25), health challenge (=0.69), and peer influence (=0.67) were major factors affecting school attendance. More than half (53.2%) of the respondents had unfavourable perception towards the school feeding programme. Results showed a significant difference between pupils’ enrolment (t = 5.332, p = 0.006) and retention rate (t = 58.386, p = 0.000) before and after the commencement of O-MEALS. Furthermore, pupils’ enrolment and retention (r = 0.993, p = 0.001) after the commencement of O-MEALS was significantly related. Food incentive was a major factor affecting school attendance, which fostered an improvement in the poor enrolment and retention previously experienced. Since a good number of the pupils possessed unfavourably perception towards the school feeding programme, it was recommended that effective monitoring be established to checkmate food vendors’ activities in delivering quality and satisfactory services. Likewise, the Government’s policies on school restructuring and levy, which had proved counterproductive, should be reviewed.

Small business sector around the world is regarded as a catalyst of employment for the largest number of people. To reduce massive unemployment and inequality in the country, the Government of South Africa introduced various initiatives to stimulate and support small businesses, the Expanded Public Works Programme (EPWP) is one of such initiatives. The enterprise development approach, is one of the delivery mechanisms of the EPWP, which seeks to transfer income to poor households in the short to medium-term. This study critically assess the impact and effectiveness of training and support interventions provided to small businesses through the EPWP. The study employs a quantitative research method and due to the size, availability and ease of access to the participants, the entire population of twenty (20) small businesses supported by the EPWP in Pretoria Region was sampled. A questionnaire-based survey was conducted. The study demonstrates that the training intervention provided through the EPWP is making positive impact and achieving its intended goals of enhancing business management skills to participants. It also reveals an interesting outcome that the majority of the participants are women. The study also identified some weaknesses in the programme which leads to the recommendation that long-term support mechanisms are essential to ensure sustainability of emerging enterprises.

Wash’Em is a process that supports humanitarians in assessing and designing rapid but context-specific hygiene programmes in crises or outbreaks. The process consists of training implementers, using tools to learn from populations, and entering findings into a software which generates contextualised activities. A process evaluation of Wash’Em use was conducted in a drought-affected area in Midland province, Zimbabwe. Data was collected during the programme design and following implementation, using a mix of qualitative methods. Findings were classified against the intended stages of Wash’Em, and the evaluation domains were defined by the UKRI Medical Research Council. The Wash’Em process was not fully implemented as intended. An abridged training was utilised, some of the tools for learning from populations were omitted, many of the recommended activities were not implemented, the delivery modalities were different from intended, the budget available was minimal, and the number of people exposed to activities were fewer than hoped. Despite these ‘on the ground’ challenges and adaptations, the Wash’Em process was considered feasible by implementers and was seen to be less top-down than most programme design approaches. The populations exposed to the intervention found the activities engaging, understood the content, and reportedly took action to improve handwashing behaviour.

BACKGROUND: The National Lung Screening Trial (ILST) and the NELSON study showed that lung cancer-specific mortality can be reduced by 20-24% using low-dose computed tomography (LDCT) screening in high-risk populations, due to an increase in early-stage diagnoses. RESEARCH QUESTION: How much lung cancer-related direct costs may be reduced using a LDCT screening programme based on the ILST protocol in a public healthcare system?STUDY DESIGN AND METHODS: Cost analysis of lung cancer screening (LCS) vs. usual care in the framework of the retail price of the Catalan public healthcare system. The LCS group included costs of screening (ILST protocol), treatment cost according to weighted average distribution of TNM staging in the NLST and NELSON trials, lung cancer detection rate (CDR) and smoking cessation intervention. The usual care group included treatment costs based on distribution of TNM staging registered in the Spanish index hospital. RESULTS: In the usual care group, treatment costs were €91,959. In the 5-year duration of the LCS programme, the average expected costs per subject were €1,342 (range €1,054-1,832 depending on malignancy in detected nodules) for screening and €32,431 for treatment, with an expected reduction of €952 based on an average CDR of 1.6%. The decrease in cost resulting from stage shift offsets 70.6% of the costs of the screening programme. INTERPRETATION: Baseline 5-year LCS costs are low according to the ILST protocol. In the Catalan public healthcare system, the expense reduction from the stage shift due to the LCS programme compensates a substantial part of its costs.

Shoulder pain is a serious clinical disease frequently related to absence from work. It’s characterized by pain and stiffness probably connected to the presence of an inflammatory substrate involving gleno-humeral capsule and collagen tissues. A physioterapy programme has shown to be effective for the conservative treatment of this disorder. Our aim is to assess if a manual treatment directed to fascial tissues could get better improvement regarding pain, strength, mobility and function. 94 healthcare workers with recurrent shoulder pain were recruited and then randomized in two groups: the control group (CG) underwent a physiotherapy programme; the study group (SG) underwent to 3 sessions of physiotherapy and to 2 session of Fascial Manipulation (FM) technique. At the end of the treatment phase, both groups improved every outcome. Despite few statistical differences between groups, at the follow up visit a greater percentage of subjects in SG overcame the minimal clinical important difference (MCID) in every outcome. We conclude that FM is effective for treatment of shoulder pain and further studies should better assess how to manage this treatment to get better results.

In the last decade, sub-Saharan African countries have taken various measures to plan for and adapt to floods in order to reduce exposure and its impacts on human health, livelihoods and infrastructure. Measuring the effects of such initiatives on social resilience is challenging as it requires to combine multiple variables and indicators that embrace thematic, spatial and temporal dimensions inherent to the resilience thinking and concept. In this research, we apply a before-after-control-intervention (BACI) evaluation to empirically measure the impacts of the “Live with Water” (LWW) project on suburban households in Dakar, Senegal. We developed a resilience index that combines anticipatory, adaptive and absorptive capacity – considered as structural dimensions – with the concept of transformative capacity – considered as a temporal reconfiguration of the first three dimensions. Our finding let us estimate that the project increased the absorptive and the anticipatory capacities by 10.61% and 4.61%, respectively. However, adaptive capacity remained unchanged. This may be explained by the fact that the programme was more successful in building drainage and physical infrastructures, rather than improving multi-level organisations and strategies to cope with existing flood events. Further flood resilience program should better combine engineering approaches with institutional change and livelihood support to poor urban dwellers.

Objectives: Studies evaluating machine learning (ML) algorithms on cross-lingual RadLex® mappings for developing context-sensitive radiological reporting tools are lacking. Therefore, we investigated whether ML-based approaches can be utilized to assist radiologists in providing key imaging biomarkers – such as The Alberta stroke programme early CT score (APECTS). Material and Methods: A stratified random sample (age, gender, year) of CT reports (n=206) with suspected ischemic stroke was generated out of 3997 reports signed off between 2015-2019. Three independent, blinded readers assessed these reports and manually annotated clinico-radiologically relevant key features. The primary outcome was whether ASPECTS should have been provided (yes/no: 154/52). For all reports, both the findings and impressions underwent cross-lingual (German to English) RadLex®-mappings using natural language processing. Well-established ML-algorithms including classification trees, random forests, elastic net, support vector machines (SVMs) and boosted trees were evaluated in a 5 x 5-fold nested cross-validation framework. Further, a linear classifier (fastText) was directly fitted on the German reports. Ensemble learning was used to provide robust importance rankings of these ML-algorithms. Performance was evaluated using derivates of the confusion matrix and metrics of calibration including AUC, brier score and log loss as well as visually by calibration plots. Results: On this imbalanced classification task SVMs showed the highest accuracies both on human-extracted- (87%) and fully automated RadLex® features (findings: 82.5%; impressions: 85.4%). FastText without pre-trained language model showed the highest accuracy (89.3%) and AUC (92%) on the impressions. Ensemble learner revealed that boosted trees, fastText and SVMs are the most important ML-classifiers. Boosted trees fitted on the findings showed the best overall calibration curve. Conclusions: Contextual ML-based assistance suggesting ASPECTS while reporting neuroradiological emergencies is feasible, even if ML-models are restricted to be developed on limited and highly imbalanced data sets.