Outcome-based Research Research Articles

Development of a Surgery-Specific Comorbidity Score for Use in Administrative Data.

To create a novel comorbidity score tailored for surgical database research. Despite their use in surgical research, the Elixhauser (ECI) and Charlson Comorbidity Indices (CCI) were developed nearly four decades ago utilizing primarily non-surgical cohorts. Adults undergoing 62 operations across 14 specialties were queried from the 2019 National Inpatient Sample (NIS) using International Classification of Diseases, 10th Revision (ICD-10) codes. ICD-10 codes for chronic diseases were sorted into Clinical Classifications Software Refined (CCSR) groups. CCSR with non-zero feature importance across four machine learning algorithms predicting in-hospital mortality were used for logistic regression; resultant coefficients were used to calculate the Comorbid Operative Risk Evaluation (CORE) score based on previously validated methodology. Areas under the receiver operating characteristic (AUROC) with 95% Confidence Intervals (CI) were used to compare model performance in predicting in-hospital mortality for the CORE score, ECI, and CCI. Validation was performed using the 2016-2018 NIS, combined 2018-2019 Florida and New York State Inpatient Databases (SID), and 2016-2022 institutional data. 699,155 records from the 2019 NIS were used for model development. The CORE score better predicted in-hospital mortality compared to the ECI within the NIS (0.90, 95%CI:0.90-0.90 vs. 0.84, 95%CI:0.84-0.84), SID (0.91, 95%CI:0.90-0.91 vs. 0.86, 95%CI:0.86-0.87), and institutional (0.88, 95%CI:0.87-0.89 vs. 0.84, 95%CI:0.83-0.85) databases (all P<0.001). Likewise, it outperformed the CCI for the NIS (0.76, 95%CI:0.76-0.76), SID (0.78, 95%CI:0.77-0.78), and institutional (0.62, 95%CI:0.60-0.64) cohorts (all P<0.001). The CORE score may better predict in-hospital mortality after surgery due to comorbid diseases in outcome-based research.

FORWARD: A Learning Framework for Logical Network Perturbations to Prioritize Targets for Drug Development.

Despite advances in artificial intelligence (AI), target-based drug development remains a costly, complex and imprecise process. We introduce F.O.R.W.A.R.D [ Framework for Outcome-based Research and Drug Development ], a network-based target prioritization approach and test its utility in the challenging therapeutic area of Inflammatory Bowel Diseases (IBD), which is a chronic condition of multifactorial origin. F.O.R.W.A.R.D leverages real-world outcomes, using a machine-learning classifier trained on transcriptomic data from seven prospective randomized clinical trials involving four drugs. It establishes a molecular signature of remission as the therapeutic goal and computes, by integrating principles of network connectivity, the likelihood that a drug's action on its target(s) will induce the remission-associated genes. Benchmarking F.O.R.W.A.R.D against 210 completed clinical trials on 52 targets showed a perfect predictive accuracy of 100%. The success of F.O.R.W.A.R.D was achieved despite differences in targets, mechanisms, and trial designs. F.O.R.W.A.R.D-driven in-silico phase '0' trials revealed its potential to inform trial design, justify re-trialing failed drugs, and guide early terminations. With its extendable applications to other therapeutic areas and its iterative refinement with emerging trials, F.O.R.W.A.R.D holds the promise to transform drug discovery by generating foresight from hindsight and impacting research and development as well as human-in-the-loop clinical decision-making.

Development of Nursing Research in Saudi Arabia: Implications for Policies and Practice.

Background: Nursing research in Saudi Arabia can be evaluated based on productivity as well as the quality of publications. The scope of scientific inquiry in nursing research expands to include clinical, health system, and outcome-based research, education, and administration. Aim: The purpose of this article is to track the development of nursing research in the Kingdom of Saudi Arabia. Design: Systematic review. Methods: This study used keywords, databases including MEDLINE, CINAHL, and PubMed to search for published articles on nursing in Saudi Arabia. The search resulted in the identification of 681 publications, from which 360 articles met the inclusion criteria and were included in the review. Results: The highest percentage of studies (56.7% of articles) focused on nursing clinical practice, and 76.0% of the studies were conducted in a hospital setting, followed by an educational setting. Most of the studies were quantitative and non-funded. More than 50.0% of the studies were first authored by Saudi scholars. Conclusions: This study concluded that nursing research in Saudi Arabia is still in its infancy, with notable improvements in the last 5 years. This correlated with an increasing number of nurses holding postgraduate degrees. With the Saudi government's strong support, the number of scientific research papers published on Saudi nursing has steadily increased over the last year.

Content Analysis of Child and Adolescent Counseling Outcome Studies in Counseling Journals

ABSTRACT This content analysis provides an overview of articles specific to outcome-based research on children and adolescents in the counseling field published in the American Counseling Association (ACA), American School Counselor Association (ASCA), and American Mental Health Counseling Associated (AMHCA) affiliated journals between the years 2012 and 2021. Results indicated that minimal articles (133 articles or 3.11%) were outcome research examining efficacy of counseling interventions across community and school settings with most of interventions being evidence-based and group-based, and the target outcome being prosocial behavior. Of 133 articles, Journal of Child and Adolescent Counseling is the most prevalent journal that published outcome research with children and adolescents (18.45%), followed by Journal of Specialist in Group Work (12.97%), and Professional School Counseling (7.04%). The paper concludes with a discussion of limitations and recommendations for future research.

Scoping Review: Interprofessional Simulation as an Effective Modality to Teaching Interprofessional Collaborative Competencies in the Emergency Department

Background: A scoping review was conducted to map the current body of research pertaining to simulation-enhanced interprofessional education (Sim-IPE) as a modality for teaching interprofessional collaboration (IPC) in the emergencydepartment (ED). Methods and Findings: The research team followed the PRISMA Extension for Scoping Reviews framework. Studies were included if they involved two or more healthcare professions, utilized simulation as the learning method for interprofessional education (IPE), involved simulation pertaining to the ED, and identified at least one Canadian Interprofessional Health Collaborative or Interprofessional Education Collaborative IPC competency as a learning outcome. In total, 896 studies were included for title and abstract screening and 806 were deemed irrelevant. Ninety full-text studies were assessed for eligibility and 34 were included in the review. Conclusions: Eighteen studies found Sim-IPE to be an effective method for teaching interprofessional competencies in the ED. Simulation-enhanced interprofessional education appears to be a promising methodology for teaching IPC competencies to ED healthcare professionals. Interprofessional collaboration competency frameworks should be utilized to guide Sim-IPE, and assessment tools specific to interprofessional competencies should be used in the assessment phase of Sim-IPE. Faculty development is a crucial component of Sim-IPE. Further longitudinal and outcome-based research is required.

The burden of bacterial antimicrobial resistance in the WHO European region in 2019: a cross-country systematic analysis

Antimicrobial resistance (AMR) represents one of the most crucial threats to public health and modern health care. Previous studies have identified challenges with estimating the magnitude of the problem and its downstream effect on human health and mortality. To our knowledge, this study presents the most comprehensive set of regional and country-level estimates of AMR burden in the WHO European region to date. We estimated deaths and disability-adjusted life-years attributable to and associated with AMR for 23 bacterial pathogens and 88 pathogen-drug combinations for the WHO European region and its countries in 2019. Our methodological approach consisted of five broad components: the number of deaths in which infection had a role, the proportion of infectious deaths attributable to a given infectious syndrome, the proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antimicrobial drug of interest, and the excess risk of mortality (or duration of an infection) associated with this resistance. These components were then used to estimate the disease burden by using two counterfactual scenarios: deaths attributable to AMR (considering an alternative scenario where infections with resistant pathogens are replaced with susceptible ones) and deaths associated with AMR (considering an alternative scenario where drug-resistant infections would not occur at all). Data were solicited from a wide array of international stakeholders; these included research hospitals, surveillance networks, and infection databases maintained by private laboratories and medical technology companies. We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We estimated 541 000 deaths (95% UI 370 000-763 000) associated with bacterial AMR and 133 000 deaths (90 100-188 000) attributable to bacterial AMR in the whole WHO European region in 2019. The largest fatal burden of AMR in the region came from bloodstream infections, with 195 000 deaths (104 000-333 000) associated with resistance, followed by intra-abdominal infections (127 000 deaths [81 900-185 000]) and respiratory infections (120 000 deaths [94 500-154 000]). Seven leading pathogens were responsible for about 457 000 deaths associated with resistance in 53 countries of this region; these pathogens were, in descending order of mortality, Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Pseudomonas aeruginosa, Enterococcus faecium, Streptococcus pneumoniae, and Acinetobacter baumannii. Methicillin-resistant S aureus was shown to be the leading pathogen-drug combination in 27 countries for deaths attributable to AMR, while aminopenicillin-resistant E coli predominated in 47 countries for deaths associated with AMR. The high levels of resistance for several important bacterial pathogens and pathogen-drug combinations, together with the high mortality rates associated with these pathogens, show that AMR is a serious threat to public health in the WHO European region. Our regional and cross-country analyses open the door for strategies that can be tailored to leading pathogen-drug combinations and the available resources in a specific location. These results underscore that the most effective way to tackle AMR in this region will require targeted efforts and investments in conjunction with continuous outcome-based research endeavours. Bill &amp; Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.

Estimating antimicrobial resistance burden in Europe—what are the next steps?

Estimating antimicrobial resistance burden in Europe—what are the next steps?

Digitization of Symbol-Denoted Blood Pressure Data From Intraoperative Paper Health Records in a Low-Middle-Income Country Using Deep Image Segmentation and Associated Postoperative Outcomes: A Feasibility Study.

In low-middle-income countries (LMICs), perioperative clinical information is almost universally collected on paper health records (PHRs). The lack of accessible digital databases limits LMICs in leveraging data to predict and improve patient outcomes after surgery. In this feasibility study, our aims were to: (1) determine the detection performance and prediction error of the U-Net deep image segmentation approach for digitization of hand-drawn blood pressure symbols from an image of the intraoperative PHRs and (2) evaluate the association between deep image segmentation-derived blood pressure parameters and postoperative mortality and length of stay. A smartphone mHealth platform developed by our team was used to capture images of completed intraoperative PHRs. A 2-stage deep image segmentation modeling approach was used to create 2 separate segmentation masks for systolic blood pressure (SBP) and diastolic blood pressure (DBP). Iterative postprocessing was utilized to convert the segmentation mask results into numerical SBP and DBP values. Detection performance and prediction errors were evaluated for the U-Net models by comparison with ground-truth values. Using multivariate regression analysis, we investigated the association of deep image segmentation-derived blood pressure values, total time spent in predefined blood pressure ranges, and postoperative outcomes including in-hospital mortality and length of stay. A total of 350 intraoperative PHRs were imaged following surgery. Overall accuracy was 0.839 and 0.911 for SBP and DBP symbol detections, respectively. The mean error rate and standard deviation for the difference between the actual and predicted blood pressure values were 2.1 ± 4.9 and -0.8 ± 3.9 mm Hg for SBP and DBP, respectively. Using the U-Net model-derived blood pressures, minutes of time where DBP <50 mm Hg (odds ratio [OR], 1.03; CI, 1.01-1.05; P = .003) was associated with an increased in-hospital mortality. In addition, increased cumulative minutes of time with SBP between 80 and 90 mm Hg was significantly associated with a longer length of stay (incidence rate ratio, 1.02 [1.0-1.03]; P < .05), while increased cumulative minutes of time where SBP between 140 and 160 mm Hg was associated with a shorter length of stay (incidence rate ratio, 0.9 [0.96-0.99]; P < .05). In this study, we report our experience with a deep image segmentation model for digitization of symbol-denoted blood pressure from intraoperative anesthesia PHRs. Our data support further development of this novel approach to digitize PHRs from LMICs, to provide accessible, curated, and reproducible data for both quality improvement- and outcome-based research.

Comprehensive selective improvements in agri-informatics semantics

The advent of information technology re-innovates all sectors of bio-sciences. Researchers use Semantic Web to improve web searching, mining and integration, which alleviates the time-consuming task of finding relevant and high-quality content. Semantics is improved through ontology engineering in any domain. Amended and developed ontologies will be uploaded to existing standardised and approved biomedical repositories. The establishment of a World Wide Web Consortium (W3C) approved and standardised ontology repository is the most ambitious goal. This work will solely focus on some selected agri-ontologies. The main objective is to promote outcome-based research and transformation styles of relevant expertise sharing. The intended goal is to win project funding to train and equip students with relevant skills and expertise. Need-based and market-oriented training and professional grooming are a tangible asset for students. The majority of traditional Web development freelancers are unaware of ontology or semantic web market demand. Freelancing is another option for expert Ontology developers. However, agriculture students are used to all the research vocabulary and terminologies in their area, but they do not know how to contribute their expertise to improve the efficiency of the Semantic Web in their domain. If the improvement in relevant ontology becomes a part of the Semantic Web, then it is termed ‘Real-time Web semantics enhancement’. In other words, the target ontology becomes a part of the future Web of meaning.

National Research Foundation (NRF): Importance of Ensuring Outcome-Based Research in India

National Research Foundation (NRF): Importance of Ensuring Outcome-Based Research in India

Validation of an on-screen application-based measurement of shoulder range of motion over telehealth medium.

Shoulder range-of-motion (ROM) assessment is vital for the follow-up evaluation of operated patients and for the outcome-based research studies. The aim of this study was to investigate the accuracy and reliability of a remote on-screen application (app)-based method of shoulder ROM measurement through a telehealth medium. A consultant shoulder surgeon, a board-certified orthopedic resident, and a graduate medical doctor served as the examiners. The cohort consisted of 24 healthy volunteers and 16 symptomatic patients with expected shoulder ROM deficits. Shoulder ROM was first examined physically using a goniometer in the clinic and then over Zoom remote conferencing using the protractor extension app of the Chrome browser. Instrument validity was examined by comparing the goniometer method with the protractor app-based method of the expert shoulder consultant using Bland-Altman analysis. It showed only minor mean differences between the healthy volunteers and the patients in elevation (2.0° and 5.0°, respectively), abduction (2.0° and 3.0°, respectively), external rotation with the elbow adducted (1.9° and 0.2°, respectively), external rotation with the elbow abducted at 90° (0.4° and 4°, respectively), and internal rotation with elbow abducted at 90° (2.3° and 1.2°, respectively), with limits of agreement that were below the well-established minimal clinically important difference values. The intraclass correlation coefficient (ICC) values varied between 0.83 and 0.96 for the volunteers and between 0.90 and 0.98 for the patients, indicating excellent correlation between the 2 methods. The interobserver reliability between 2 examiners for the protractor app-based method as evaluated by ICC scores was excellent; it ranged between 0.86 and 0.98 for the volunteers and between 0.88 and 0.99 for the patients. Comparison of the protractor app-based method with the gold-standard goniometer method for the resident and the graduate doctor showed excellent ICC values. A protractor app-based method of measuring shoulder ROM over a telehealth medium is accurate and reliable compared with a clinical goniometer method. This validated method can be used during remote telehealth consultation with significant benefits of saving patients travel and time during the COVID-19 (coronavirus disease 2019) pandemic and even later.

Protecting the infant-parent relationship: special emphasis on perinatal mood and anxiety disorder screening and treatment in neonatal intensive care unit parents.

Perinatal mood and anxiety disorders (PMADs) are common, particularly among parents of infants requiring admission to the neonatal intensive care unit (NICU), yet remain underdiagnosed and undertreated. Undertreated parental mental health disorders can interfere with healthy infant development, compounding abnormal neurodevelopment and psychosocial development that preterm or ill newborns may already face. Interdisciplinary efforts to increase PMAD awareness, screening, and referral uptake may improve family-infant health and developmental outcomes in high-risk infants requiring NICU admission. Therefore, special emphasis on PMAD screening and treatment in NICU parents aligns with the American Academy of Pediatrics mission and should be a focus in neonatal care and included in education, quality improvement, and outcome-based research initiatives.

Development of algorithms for identifying patients with Crohn’s disease in the Japanese health insurance claims database

Real-world big data studies using health insurance claims databases require extraction algorithms to accurately identify target population and outcome. However, no algorithm for Crohn's disease (CD) has yet been validated. In this study we aim to develop an algorithm for identifying CD using the claims data of the insurance system. A single-center retrospective study to develop a CD extraction algorithm from insurance claims data was conducted. Patients visiting the Kitasato University Kitasato Institute Hospital between January 2015-February 2019 were enrolled, and data were extracted according to inclusion criteria combining the Tenth Revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) diagnosis codes with or without prescription or surgical codes. Hundred cases that met each inclusion criterion were randomly sampled and positive predictive values (PPVs) were calculated according to the diagnosis in the medical chart. Of all cases, 20% were reviewed in duplicate, and the inter-observer agreement (Kappa) was also calculated. From the 82,898 enrolled, 255 cases were extracted by diagnosis code alone, 197 by the combination of diagnosis and prescription codes, and 197 by the combination of diagnosis codes and prescription or surgical codes. The PPV for confirmed CD cases was 83% by diagnosis codes alone, but improved to 97% by combining with prescription codes. The inter-observer agreement was 0.9903. Single ICD-code alone was insufficient to define CD; however, the algorithm that combined diagnosis codes with prescription codes indicated a sufficiently high PPV and will enable outcome-based research on CD using the Japanese claims database.

Corporate Socio-Political Activism and its Effects on Corporate Valuation

The field of Corporate Socio-Political Activism (CSA) literature is gaining attention as multinational corporations (MNCs) are increasingly taking stances in support or opposition of socio-political issues. This study aims to resolve a lack of outcome-based research in the CSA literature. It conducts a comparative examination of short-term consequences of CSA on corporate valuation, investigating diverging CSA stance intensities concentrating on the Black Lives Matter movement. Through the theoretical lenses of agency theory and signalling theory, this study explores shareholder reactions to CSA stances to provide strategic insights for theoretical and managerial practice. The event study focusses on public corporations listed in the S&P100 index, which issued statements concerning the socio-political issue of the Black Lives Matter. In order to assess and quantify the intensity and characteristics of CSA stances, We create a disclosure index through a grounded-theory approach. Two key findings emerge from this study: (1) CSA stances in this environment are not inherently value-creating, nor value-destructing in the short-term, yet (2) the reputation for previous social performance positively moderates the immediate response of the shareholder to a CSA stance.

Patient Factors That Matter in Predicting Hip Arthroplasty Outcomes: A Machine-Learning Approach

BackgroundDespite the success of total hip arthroplasty (THA), approximately 10%-15% of patients will be dissatisfied with their outcome. Identifying patients at risk of not achieving meaningful gains postoperatively is critical to pre-surgical counseling and clinical decision support. Machine learning has shown promise in creating predictive models. This study used a machine-learning model to identify patient-specific variables that predict the postoperative functional outcome in THA. MethodsA prospective longitudinal cohort of 160 consecutive patients undergoing total hip replacement for the treatment of degenerative arthritis completed self-reported measures preoperatively and at 3 months postoperatively. Using four types of independent variables (patient demographics, patient-reported health, cognitive appraisal processes and surgical approach), a machine-learning model utilizing Least Absolute Shrinkage Selection Operator (LASSO) was constructed to predict postoperative Hip Disability and Osteoarthritis Outcome Score (HOOS) at 3 months. ResultsThe most predictive independent variables of postoperative HOOS were cognitive appraisal processes. Variables that predicted a worse HOOS consisted of frequent thoughts of work (β = −0.34), frequent comparison to healthier peers (β = −0.26), increased body mass index (β = −0.17), increased medical comorbidities (β = −0.19), and the anterior surgical approach (β = −0.15). Variables that predicted a better HOOS consisted of employment at the time of surgery (β = 0.17), and thoughts related to family interaction (β = 0.12), trying not to complain (β = 0.13), and helping others (β = 0.22). ConclusionsThis clinical prediction model in THA revealed that the factors most predictive of outcome were cognitive appraisal processes, demonstrating their importance to outcome-based research. Level of evidencePrognostic Level 1.

Are we prepared for the future? A mixed-method study on quality management in decentralized family medicine teaching

ABSTRACT In Germany, two-week clinical clerkships in university-associated general practices have existed since 2002. Approximately 10,000 medical students participate in these decentral clerkships each year. Empirical information on quality management strategies in decentral learning environments is sparse. This nationwide study aims to describe the current quality management efforts of German family medicine departments in response to negative events. A nationwide three-part mixed methods study on the quality management of family medicine clerkships was conducted. First, individuals from n = 37 family medicine departments involved in the organization of family medicine clerkships were interviewed. Interview transcripts were analyzed with qualitative content analysis. Second, a questionnaire on quality management of decentralized learning environments based on the categories of the analysis was developed and sent to the departments. Three negative event cases in family medicine clerkships were included in the questionnaire. Third, interview and survey data were integrated based on respondents’ process descriptions of how each department handled the cases. Of the 37 contacted departments, n = 12 (32%) performed an interview. Major categories of negative events included problems in the student-teacher interaction, didactical challenges, and problematic student behavior. Twenty departments answered at least one questionnaire (54%). Most respondents indicated that their department conducts quality management in decentralized teaching. Negative events in decentral family medicine clerkships occurred at a rate of 66.4 to 179.5 events per 10,000 Students per semester. The mixed-method analysis showed that departments are conscious about quality management issues in decentral learning environments but adhere to heterogeneous local standards. Negative events occur regularly in decentral learning environments. Local quality management processes exist but lack national harmonization. Further outcome-based research is needed to explore the effectiveness and feasibility of quality management strategies. This will become increasingly relevant with an expected upscaled family medicine content.

Who Decides? Toward a Typology of Transit Governance

This article describes a typology for formal governance structures of public transit in the United States to support inquiry into how organizational structures influence policy making processes, organizational capacity and policy outcomes. Scholarship of public transit has largely explored outcome-based research while paying less attention to how decisions are made. Despite some transport scholarship that shows how institutional characteristics influence financing, power arrangements and public discourse, there has been little recent analysis of governance within public transit systems beyond the regional role of Metropolitan Planning Organizations (MPOs). Using data from multiple sources, we assembled a database of governance structure of transit systems in the largest 40 cities in the United States. We show that the structure of transit decision making has substantial variance across and within cities, and is far from limited to MPOs. The variety of governance models and growth of local and sub-local models suggest that local context is critical for better understanding transit priorities and decision-making processes.

Optimal Reconstruction of Sagittal Alignment According to Global Alignment And Proportion Score Can Reduce Adjacent Segment Degeneration After Lumbar Fusion.

A retrospective study. The aim of this study was to investigate the ability of Global Alignment and Proportion (GAP) score to predict the occurrence of adjacent segment degeneration (ASD) after fusion surgery for lumbar degenerative diseases. The recently developed GAP score was applied to predict postoperative complications for adult spinal deformity, as well as to facilitate future outcome-based research on optimal treatment for various spinal conditions. However, it remains unclear whether reconstruction of alignment according to GAP score can reduce the ASD rates. This study retrospectively reviewed 126 consecutive patients who had undergone lumbar fusion and had been followed over 2 years. Pre- and postoperative radiographs and MRI were analyzed for ASD. GAP scores were calculated based on the early postoperative spinopelvic parameters. Cochran-Armitage test of trend was performed to investigate the association between GAP score and the occurrence of ASD. Receiver-operating characteristic curves were used to analyze the predictive accuracy of the GAP score for ASD. Radiographical ASD (R-ASD) and symptomatic ASD (S-ASD) were diagnosed in 44 (34.9%) patients and in 13 (10.3%) patients, respectively. The patients with a proportioned spinopelvic state according to the GAP score had significantly lower rates of ASD (R-ASD and S-ASD) or S-ASD than those with a moderately or severely disproportioned spinopelvic state. The area under curve for the GAP score predicting ASD and S-ASD was 0.691 (95% confidence interval [CI]: 0.596∼0.785, P < 0.01) and 0.865 (95% CI: 0.771∼0.958, P < 0.01), respectively. Our study revealed a significant association between postoperative GAP score and occurrence of ASD after lumbar fusion surgery. Setting surgical goals according to the GAP score may help reduce the occurrence of ASD, especially for S-ASD.Level of Evidence: 4.

Cash transfer and professional care for tackling child poverty and neglect in Italy

AbstractThere has been a general increase in poverty over the last decade in Italy, which has mainly affected the younger generations, with children and youth experiencing the worst economic conditions. This is primarily not due to a lack of available economic resources but to the way in which these resources are allocated: mainly in the form of cash transfers rather than services. The provision of adequate services based on professional work needs to be implemented by overcoming two main obstacles which are highlighted by the results of two studies presented here. The first study concerns the quality of professional care and the systematic use of outcome evaluation, the second concerns the vision of professionals and their ability to integrate the provision of services with economic support aimed at improving children’s growth and parenting skills. The two studies were carried out as part of an international debate on how to effectively fight poverty and social exclusion of children which was promoted by the International Association for Outcome-based Evaluation and Research on Family and Children’s Services (iaOBERfcs).

Improvement in Student Performance and Perceptions through a Flipped Anatomy Classroom: Shifting from Passive Traditional to Active Blended Learning.

One of the major challenges facing anatomy educators is delivering the anatomy materials in fewer hours with a reduction of anatomy courses in the integrated curricula. The flipped classroom modality may be an innovative solution. However, its effectiveness remains under debate due to a lack of outcome-based research and the mixed results of students' performance. The present study aimed to determine the outcome of the flipped classroom based upon the level of student cognition. The study investigated performance on 17 multiple-choice anatomy questions as a part of the final examination of the musculoskeletal system module. The results were compared between the first-year female students of Qassim Medical College, specifically the flipped classroom group (46 students) of the academic year (2018-2019) and the traditional group (49 students) of the academic year (2017-2018). The mean differences in the students' grades on the anatomy questions at the level of knowledge, application, and analysis using Cohen's d test were 0.43, 1.41, and 1.01, respectively. These results suggest the positive impact of flipping the students' classrooms on improving their levels of thinking according to Bloom's taxonomy. Perception surveys also revealed students' enthusiasm for the pre-class activities, leading to a better performance in the class with more engagement with their peers and teachers. The present study suggested that the flipped classroom modality can be performed to compensate for the reduction of anatomy educational hours. However, further studies are recommended to investigate the best practices of the flipped classroom that fit with the students' needs and workloads.