Improving case study research in medical education: A systematised review

Context:Case study research (CSR) is a research approach that guides holistic investigation of a real phenomenon. This approach may be useful in medical education to provide critical analyses of teaching and learning, and to reveal the underlying elements of leadership and innovation. There are variations in the definition, design and choice of methods, which may diminish the value of CSR as a form of inquiry.Objectives:This paper reports an analysis of CSR papers in the medical education literature. The review aims to describe how CSR has been used and how more consistency might be achieved to promote understanding and value.Methods:A systematised review was undertaken to quantify the number of CSR articles published in scholarly medical education journals over the last 10 years. A typology of CSR proposed by Thomas and Myers to integrate the various ways in which CSR is constructed was applied.Results:Of the 362 full‐text articles assessed, 290 were excluded as they did not meet the eligibility criteria; 76 of these were titled ‘case study’. Of the 72 included articles, 50 used single‐case and 22 multi‐case design; 46 connected with theory and 26 were atheoretical. In some articles it was unclear what the subject was or how the subject was being analysed.Conclusions:In this study, more articles titled ‘case study’ failed than succeeded in meeting the eligibility criteria. Well‐structured, clearly written CSR in medical education has the potential to increase understanding of more complex situations, but this review shows there is considerable variation in how it is conducted, which potentially limits its utility and translation into education practice. Case study research might be of more value in medical education if researchers were to follow more consistently principles of design, and harness rich observation with connection of ideas and knowledge to engage the reader in what is most interesting

Effect size for single-subject design in phonological treatment

Purpose: To document, validate, and corroborate effect size (ES) for single-subject design in treatment of children with functional phonological disorders; to evaluate potential child-specific contributing variables relative to ES; and to establish benchmarks for interpretation of ES for the population. Method: Data were extracted from the Developmental Phonologies Archive for 135 preschool children with phonological disorders who previously participated in single-subject experimental treatment studies. Standard Mean DifferenceAll with Correction for Continuity was computed to gauge the magnitude of generalization gain that accrued longitudinally from treatment for each child, with the data aggregated for purposes of statistical analyses. Results: ES ranged from 0.09 to 27.83 for the study population. ES was positively correlated with conventional measures of phonological learning and visual inspection of learning data based on procedures standard to single-subject design. ES was linked to children’s performance on diagnostic assessments of phonology, but not other demographic characteristics or related linguistic skills and nonlinguistic skills. Benchmarks for interpretation of ES were estimated as 1.4, 3.6, and 10.1 for small, medium, and large learning effects, respectively. Conclusion: Findings have utility for single-subject research and translation of research to evidence-based practice for children with phonological disorders.National Institutes of Health DC00433, RR7031K, DC00076, DC001694 (PI: Gierut

Universal Design for Learning in K-12 Educational Settings: A Review of Group Comparison and Single-subject Intervention Studies

This literature review on Universal Design for Learning (UDL) included articles from January 1984 through June 2014. We (a) investigated the UDL educational framework without the inclusion of other major K-12 educational frameworks in learning environments, (b) reported researchers’ scope and depth of use of the UDL principles, and (c) focused our investigation on two research methods: group comparison and single-subject. We used the quality indicators for evidence-based practices (EBPs) in special education to review, not rate, the final pool of five peer-reviewed articles. Results included analyses of the incorporation of UDL principles in all identified studies, highlighting the need for caution in promoting conceptual frameworks until sufficient empirical evidence is available to validate pedagogical utility in educational environments. We conclude that the UDL framework has merit but researchers must conduct studies that use group comparison and single-subject studies to independently test the UDL principles, guidelines, and checkpoints to increase the likelihood of causation in treatment outcomes

Optimising experimental design for MEG resting state functional connectivity measurement

The study of functional connectivity using magnetoencephalography (MEG) is an expanding area of neuroimaging, and adds an extra dimension to the more common assessments made using fMRI. The importance of such metrics is growing, with recent demonstrations of their utility in clinical research, however previous reports suggest that whilst group level resting state connectivity is robust, single session recordings lack repeatability. Such robustness is critical if MEG measures in individual subjects are to prove clinically valuable. In the present paper, we test how practical aspects of experimental design affect the intra-subject repeatability of MEG findings; specifically we assess the effect of co-registration method and data recording duration. We show that the use of a foam head-cast, which is known to improve co-registration accuracy, increased significantly the between session repeatability of both beamformer reconstruction and connectivity estimation. We also show that recording duration is a critical parameter, with large improvements in repeatability apparent when using ten minute, compared to five minute recordings. Further analyses suggest that the origin of this latter effect is not underpinned by technical aspects of source reconstruction, but rather by a genuine effect of brain state; short recordings are simply inefficient at capturing the canonical MEG network in a single subject. Our results provide important insights on experimental design and will prove valuable for future MEG connectivity studies

Network Utility Maximization under Maximum Delay Constraints and Throughput Requirements

We consider the problem of maximizing aggregate user utilities over a multi-hop network, subject to link capacity constraints, maximum end-to-end delay constraints, and user throughput requirements. A user's utility is a concave function of the achieved throughput or the experienced maximum delay. The problem is important for supporting real-time multimedia traffic, and is uniquely challenging due to the need of simultaneously considering maximum delay constraints and throughput requirements. We first show that it is NP-complete either (i) to construct a feasible solution strictly meeting all constraints, or (ii) to obtain an optimal solution after we relax maximum delay constraints or throughput requirements up to constant ratios. We then develop a polynomial-time approximation algorithm named PASS. The design of PASS leverages a novel understanding between non-convex maximum-delay-aware problems and their convex average-delay-aware counterparts, which can be of independent interest and suggest a new avenue for solving maximum-delay-aware network optimization problems. Under realistic conditions, PASS achieves constant or problem-dependent approximation ratios, at the cost of violating maximum delay constraints or throughput requirements by up to constant or problem-dependent ratios. PASS is practically useful since the conditions for PASS are satisfied in many popular application scenarios. We empirically evaluate PASS using extensive simulations of supporting video-conferencing traffic across Amazon EC2 datacenters. Compared to existing algorithms and a conceivable baseline, PASS obtains up to $100\%$ improvement of utilities, by meeting the throughput requirements but relaxing the maximum delay constraints that are acceptable for practical video conferencing applications

Algorithmic Bayesian Persuasion

Persuasion, defined as the act of exploiting an informational advantage in order to effect the decisions of others, is ubiquitous. Indeed, persuasive communication has been estimated to account for almost a third of all economic activity in the US. This paper examines persuasion through a computational lens, focusing on what is perhaps the most basic and fundamental model in this space: the celebrated Bayesian persuasion model of Kamenica and Gentzkow. Here there are two players, a sender and a receiver. The receiver must take one of a number of actions with a-priori unknown payoff, and the sender has access to additional information regarding the payoffs. The sender can commit to revealing a noisy signal regarding the realization of the payoffs of various actions, and would like to do so as to maximize her own payoff assuming a perfectly rational receiver. We examine the sender's optimization task in three of the most natural input models for this problem, and essentially pin down its computational complexity in each. When the payoff distributions of the different actions are i.i.d. and given explicitly, we exhibit a polynomial-time (exact) algorithm, and a "simple" $(1-1/e)$-approximation algorithm. Our optimal scheme for the i.i.d. setting involves an analogy to auction theory, and makes use of Border's characterization of the space of reduced-forms for single-item auctions. When action payoffs are independent but non-identical with marginal distributions given explicitly, we show that it is #P-hard to compute the optimal expected sender utility. Finally, we consider a general (possibly correlated) joint distribution of action payoffs presented by a black box sampling oracle, and exhibit a fully polynomial-time approximation scheme (FPTAS) with a bi-criteria guarantee. We show that this result is the best possible in the black-box model for information-theoretic reasons

Effectiveness of a Wearable Fitness Tracker: Practice Implications in Allied Health -- a Single Case Study

ABSTRACT Purpose: The purpose of this single case study was to examine the effectiveness of utilizing a wearable fitness tracker device in self-monitoring behavior change in complying with prescribed treatment. It was hypothesized that using a wearable self-monitoring device while involved in the treatment of multiple medical conditions will be beneficial to behavioral compliance and behavioral change in an overweight type 2 diabetic, geriatric subject being treated by a multidisciplinary health team utilizing an integrated treatment model. Methods: An exploratory single case study research design is employed to explore those situations in which the intervention, a wearable monitoring device, is employed. This observational case study model was applied to the utility of self-monitoring wearable smart technology marketed as FitBit for self-monitoring activity and exercise over a 36-week period. Results: After the 36-week intervention program, results revealed qualitative improvements in the (1) active minutes, (2) steps taken and (3) miles walked by the subject. In addition, the technology reported on calories burned, sleep hours and minutes logged and liquid consumed during each 24-hour period. The integrated allied health team was able to monitor changes made over time and noted improved time dedicated to exercise, walking and total miles walked. On miles walked per day, the results show that the subject increased miles walked from less than one mile per day to more than 4.6 miles per day which approached the recommended 5 miles per day goal. The calorie monitor aided the subject in changing calorie intake from that which exceeded 3500 per day to the recommended 2500 calories per day. Furthermore, the hours slept per night changed from less than 6.8 per night to 8.1 per day. Conclusions: It was concluded that a 36-week intervention program can be an effective intervention methodology for improved health and well-being as measured by standard healthcare and wellness markers utilizing a wearable fitness tracker device like the Fitbit. While further research is needed with a larger sample, it is therefore recommended that allied health professionals consider the utilization of wearable smart technology as an adjunct to self-monitoring and compliance with treatment planning and follow-up care with allied health care providers