Simulation & Gaming

Healthcare providers require multitasking and multi-patient care skills, and training programs do not formally incorporate curricula specifically for multitasking skills to trainees. The medical education community is in equipoise on whether multitasking ability is a fixed trait. Furthermore, it is unclear whether multitasking ability affects those who gravitate toward careers that demand it, particularly among medical students deciding on a specialty. We sought to define the association between specialty choice, multitasking abilities and multi-patient care delivery among pre-clinical medical students. For this study, we examined both efficiency and accuracy metrics within multitasking and whether they were different between students choosing specialties.

This was a planned cross-sectional sub-study focused on 2nd year medical students (MS-IIs) within a parent study evaluating multi-patient care skills using a serious game (VitalSigns:ED^TM) depicting a pediatric emergency department. Subjects completed a Multitasking Ability Test (MTAT) and five VitalSigns:ED gameplays. The predictor variable was specialty choice, categorized into multitasking and non-multitasking groups. Outcome variables measuring efficiency and diagnostic accuracy were obtained from the MTAT and the game. The primary analysis was a Mann–Whitney U test, and secondary analyses employed Spearman Rank correlations.

Twelve students applied to multitasking specialties and 18 applied to others. Those in the multitasking specialties had faster MTAT completions than the other cohort (29.8 vs. 59.7 sec, 95%CI difference -0.9 to -39.8 sec). Differential diagnoses were higher in multitasking specialties in VitalSigns:ED (2.03 vs. 1.06, 95%CI difference +0.05 to +1.54) but efficiency metrics in the game did not differ.

Multitasking and multi-patient care performance show some association with preferred specialty choices for MS-IIs prior to clinical exposure.

Patient safety and improved outcomes are core priorities in healthcare, and effective handoffs are essential to these priorities. Validating handoff tools using simulation is a novel approach.

The construct validity and instrument reliability of the I-BIDS^© tool were tested. In Phase I, construct validity was substantiated with a convenience sample of 21 healthcare providers through an electronic survey. Content Validity Ratio (CVR) was tabulated using Lawshe’s CVR. Interrater reliability was tested in a simulated handoff scenario, in Phase II, with graduate nursing students and two raters, and simulation effectiveness was assessed by students.

Construct validity was evaluated, and 17 of the 25 items were found significant at the critical level (0.42). Items scoring below were removed, and the tool was reduced by one category. Weighted kappa (Kw) with quadratic weights was run from the scenario data to determine if there was an agreement between raters of handoff performance. There was a statistically significant agreement between the two raters, Kw = .627 (95% CI: .549–.705), p < .001) with good strength of the agreement. SET-M Total mean was 55.64 (SD = 2.46).

The tool showed beginning validity and interrater reliability. The SET-M Learning subscale showed the widest range of scores which suggests the most opportunity for improvement. Use of the tool in simulated scenarios may be one way to test the items further.

Simulation was effective in facilitating the evaluation of the tool.

Aging people can suffer from cognitive impairments with a range of symptoms, including memory, perception, and difficulty in solving problems called Alzheimer’s disease (AD). The early detection of Mild Cognitive Impairment (MCI), which can develop AD, plays a major role in the management of patients to slow the decline in cognitive function, as treatments are effective at an early stage of the disease course. For this purpose, advanced computer technologies can provide a tool for the early detection of AD and prediction of disease progression. This article presents a serious game, including 16 mini-games that aimed at detecting AD or MCI in the mild stage. Based on gamification techniques and machine learning (ML), by overcoming the limitations of traditional tests. This gamified cognitive tool, entitled AlzCoGame, evaluates the main cognitive domains considered to be the most pertinent indicators in diagnosing cognitive impairments: working memory, episodic memory, executive functions, Visio-spatial orientation, concentration, and attention.

Six predictive ML models have been implemented using the AlzCoGame dataset. We used the K-fold cross-validation and classification metrics to validate the model's performance. Based on the results of the pilot study, the best overall performance was obtained by the RF classifier with average Sensitivity = 0.89, Specificity = 0.93, Accuracy = 0.92, F1-Score = 0.91, and ROC = 0.91. We can deduce that including machine learning techniques and serious games could help improve certain aspects of the clinical diagnosis of cognitive impairment. Moreover, clinical trials are required to prove the impact of this gamified program on cognitive skills and evaluate usability measures.

Common definitions of rhetoric in games such as Bogost’s ‘procedural rhetoric’ have their basis in the Aristotelian definition of rhetoric, which concerns itself with discovering all means of persuasion in language.

Gaming rhetoric has more to do with inducing action in players, and therefore falls more in line with Kenneth Burke’s definition of rhetoric. Grinding is a gaming mechanic that can be analysed using rhetorical devices if Burke’s definition of rhetoric is held at the core of this understanding. This article posits that games that employ a particular game mechanic, that of ‘grinding’, are relying on a specific rhetorical device in their design known as ploke, which then persuades the player to continue to do an action multiple times over, and therefore persuade players to form attitudes that align with the designer’s rhetorical goals.

An analysis of ploke was applied to three specific games: Runescape (2001), Hades (2018) and Animal Crossing: New Horizons (2020). These games were chosen based on the ability to look at multiple genres as well as multiple different points in modern game development history. Ploke provided the ability to understand the method in which grinding communicates with players, enticing and incentivizing them to continue to complete actions repeatedly, whether for story progression or skill enhancement. The rhetorical power of ploke is found in its repetition, and since ploke describes the use of repetition in rhetorical contexts, thus grinding’s rhetorical power can be explained through this rhetorical phenomenon.

Ploke is just one rhetorical device, and grinding is just one game mechanic. There are several other game mechanics that can be analysed through rhetorical devices. This analysis allows researchers in interdisciplinary fields of games and linguistics, communication or humanities to explore how games communicate and influence player decisions.