We provide an alternative psychometric perspective on the empirical statistical dependencies observed between response accuracy residuals (RARs) and response time residuals (RTRs) in the context of the van der Linden model. This perspective emphasizes the RAR (or parts of the RAR) as being exogenous and having a directional influence on response time. Our simple and theoretically justifiable perspective adds to previous joint response time/accuracy models and comports with recent generalizations of the D-diffusion IRT model incorporating person-by-item interactions, and thus similarly reproduces many of the recently highlighted empirical findings concerning the associations between RARs and RTRs. Using both empirical and simulation-based results, we show how our psychometric perspective has both applied and interpretational implications. Specifically, it would suggest that (1) studies of item parameter estimate heterogeneity in relation to response times may reflect more of a psychometric artifact (due to the exogenous effects of the RARs) as opposed to providing insights about the response process (e.g., the application of different response strategies) and that (2) efforts to use RTRs as indicators of latent proficiency should attend to the anticipated interactions between the latent proficiency and RAR on response times. The validity of our psychometric perspective against alternatives likely relies on appeals to theory; the best perspective to take may vary depending on the test setting.

UNASSIGNED: The relationship between post-concussion kinesiophobia and clinical and functional reaction time (RT) beyond clinical recovery remains to be elucidated.
UNASSIGNED: College-aged participants with (n = 20) and without (n = 20) a concussion history completed patient-reported outcomes, and RT tasks. Kinesiophobia, symptoms and RTs were compared using t-tests. Linear regressions were performed to determine if kinesiophobia predicted RT measures and dual-task cost.
UNASSIGNED: The concussion history group reported higher scores (p < 0.01) for all patient-reported outcomes. We observed significant single-task RT differences between groups (p = 0.013) such that those without a concussion history (m = 0.51s ± 0.08) were faster (m = 0.59s ± 0.12). There were no clinical or dual-task RT differences between groups (p > 0.05). Kinesiophobia significantly predicted single-task RT (R2 = 0.22).
UNASSIGNED: Kinesiophobia should be considered when measuring RT.

This study reports the performance of 7 types of consumer grade passive Electronic Radon Integrating Monitors, ERIM (AlphaE, AER Plus, Canary, Corentium Pro, Radon Scout Home, Ramon and Wave) and passive etched track radon detectors. All monitors and passive radon detectors were exposed side by side for 2 periods of 3 months under controlled conditions in the UKHSA radon chamber and in a stainless steel container to an average radon concentration of 4781 Bq m-3and 166 Bq m-3, respectively. The performance of each individual monitor was compared with Atmos 12DPX and AlphaGUARD P30 reference instruments. The performance of the monitors was evaluated by estimating the biased, precision and measurement errors of each type. It was found that UKHSA passive radon detectors showed excellent performance (measurement error < 10%) at both higher and lower exposures. The AlphaE, Canary and Ramon showed excellent performance, with measurement error <10%, when they were exposed to radon concentrations between 4000 Bq m-3and 6000 Bq m-3in the UKHSA radon chamber. However, when the monitors were exposed to radon levels below the UK radon Action Level of 200 Bq m-3, the only ERIM which had a measurement error <10% was the Radon Scout Home. All other monitors showed a significant decrease in their performance with measurement errors ranging between 20% and 50%. The calibration factor, which is the ratio between the measured value (background is subtracted) and the reference value, was also studied. It was found that the calibration factors of individual monitors changed significantly. Calibration measurements in 2019 and in 2023 found that the percentage change varied between -46% and +63%. This shows the importance of initial and regular calibration, and maintenance of the monitors.

Utilizing computer-based scales for cognitive and psychological evaluations allows for the collection of objective data, such as response time. This cross-sectional study investigates the significance of response time data in cognitive and psychological measures, with a specific focus on its role in evaluating sleep quality through the Insomnia Severity Index (ISI) scale. A mobile application was designed to administer scale tests and collect response time data from 2729 participants. We explored the relationship between symptom severity and response time. A machine learning model was developed to predict the presence of insomnia symptoms in participants using response time data. The result revealed a statistically significant difference (p < 0.01) in the total response time between participants with or without insomnia symptom. Furthermore, a strong correlation was observed between the severity of specific insomnia aspects and the response times at the individual questions level. The machine learning model demonstrated a high predictive Area Under the ROC Curve (AUROC) of 0.824 in predicting insomnia symptoms based on response time data. These findings highlight the potential utility of response time data to evaluate cognitive and psychological measures.

Out-of-hospital cardiac arrest (OHCA) is a time-sensitive medical emergency that needs immediate interventions. COVID-19 affected the performance of the emergency medical service (EMS) system in pre-hospital care, including the management of cardiac arrest. This study aimed to identify the impact of the COVID-19 pandemic on pre-hospital management of out-of-hospital cardiac arrest and its outcome in Qom City, Iran. In this descriptive-analytical study, the data were collected from the electronic registration system of the EMS center in Qom, Iran. All OHCA patients who received resuscitation during COVID-19 and before COVID-19 were enrolled in the study. Data consisted of the characteristics of OHCA patients, EMS interventions and response times, and the outcome of OHCA. A P-value of <0.05 was deemed statistically significant. 630 OHCA patients in the COVID-19 period and 524 OHCA patients in the pre-COVID-19 period were included in the study. Endotracheal intubation and defibrillation were done more in the COVID-19 period than in the pre-COVID-19 period (50.2 % vs. 17 %, p<0.001 %, and 40.1 % vs. 22.5 %, p < 0.001, respectively). The EMS response time was longer during the COVID-19 pandemic (9.1 ± 3.9 min vs. 7.6 ± 1.4 min, p < 0.001). The rate of pre-hospital return of spontaneous circulation (ROSC) was lower in the COVID-19 period (15.6 % vs. 8.4 %, p < 0.001). According to univariate analysis, ROSC was predicted by COVID-19 (p < 0.001). However, COVID-19 was not the statistically significant independent predictor after multivariate analysis (p < 0.67). The COVID-19 pandemic period influenced OHCA and ROSC. Also, it affected pre-hospital management in the OHCA situation. The negative impact of COVID-19 on the EMS response reflected the need to know and remove barriers to managing crises such as COVID-19.

BACKGROUND: Emotional clarity has often been assessed with self-report measures, but efforts have also been made to measure it passively, which has advantages such as avoiding potential inaccuracy in responses stemming from social desirability bias or poor insight into emotional clarity. Response times (RTs) to emotion items administered in ecological momentary assessments (EMAs) may be an indirect indicator of emotional clarity. Another proposed indicator is the drift rate parameter, which assumes that, aside from how fast a person responds to emotion items, the measurement of emotional clarity also requires the consideration of how careful participants were in providing responses.
OBJECTIVE: This paper aims to examine the reliability and validity of RTs and drift rate parameters from EMA emotion items as indicators of individual differences in emotional clarity.
METHODS: Secondary data analysis was conducted on data from 196 adults with type 1 diabetes who completed a 2-week EMA study involving the completion of 5 to 6 surveys daily. If lower RTs and higher drift rates (from EMA emotion items) were indicators of emotional clarity, we hypothesized that greater levels (ie, higher clarity) should be associated with greater life satisfaction; lower levels of neuroticism, depression, anxiety, and diabetes distress; and fewer difficulties with emotion regulation. Because prior literature suggested emotional clarity could be valence specific, EMA items for negative affect (NA) and positive affect were examined separately.
RESULTS: Reliability of the proposed indicators of emotional clarity was acceptable with a small number of EMA prompts (ie, 4 to 7 prompts in total or 1 to 2 days of EMA surveys). Consistent with expectations, the average drift rate of NA items across multiple EMAs had expected associations with other measures, such as correlations of r=-0.27 (P<.001) with depression symptoms, r=-0.27 (P=.001) with anxiety symptoms, r=-0.15 (P=.03) with emotion regulation difficulties, and r=0.63 (P<.001) with RTs to NA items. People with a higher NA drift rate responded faster to NA emotion items, had greater subjective well-being (eg, fewer depression symptoms), and had fewer difficulties with overall emotion regulation, which are all aligned with the expectation for an emotional clarity measure. Contrary to expectations, the validities of average RTs to NA items, the drift rate of positive affect items, and RTs to positive affect items were not strongly supported by our results.
CONCLUSIONS: Study findings provided initial support for the validity of NA drift rate as an indicator of emotional clarity but not for that of other RT-based clarity measures. Evidence was preliminary because the sample size was not sufficient to detect small but potentially meaningful correlations, as the sample size of the diabetes EMA study was chosen for other more primary research questions. Further research on passive emotional clarity measures is needed.

Shortening the prehospital emergency medical service (EMS) response time is crucial for saving lives and lowering mortality and disability rates in patients with sudden illnesses. Descriptive analyses of prehospital EMS response time and each component were conducted separately using ambulance trip data from the 120 Dispatch Command Centre in the main urban area of Chongqing in 2021, and then, logistic regression analyses were used to explore the influencing factors. The median prehospital EMS response time in the main urban area of Chongqing was 14.52 min and the mean was 16.14 min. A 44.89% of prehospital EMS response time exceeded 15 min. Response time was more likely to surpass this threshold during peak hours and in high population density areas. Conversely, lower probabilities exceeding 15 min were observed during the night shift, summer, and autumn seasons, and areas with a high density of emergency station. 33.28% of preparation time was >3 min, with the night shift and high population density areas more likely to be >3 min, while for the summer and autumn seasons, high Gross National Product (GDP) per capita areas had a lower likelihood of having preparation time >3 min. 45.52% of travel time was >11 min, with peak hours, summer and autumn, and high GDP per capita areas more likely to have had a travel time >11 min, while night shift and high emergency station density areas had a lower likelihood of travel time >11 min. The primary factors influencing prehospital EMS response time were shifts, traffic scenarios, seasons, GDP per capita, emergency station density, and population density. Relevant departments can devise effective interventions to reduce response time through resource allocation and department coordination, staff training and work arrangement optimisation, as well as public participation and education, thereby enhancing the efficiency of prehospital emergency medical services.

UNASSIGNED: This study investigates the log data and response behavior from invigilated in-person electronic timed exams at the University of Veterinary Medicine Hannover, Foundation, Germany. The primary focus is on understanding how various factors influence the time needed per exam item, including item format, item difficulty, item discrimination and character count. The aim was to use these results to derive recommendations for designing timed online distance examinations, an examination format that has become increasingly important in recent years.
UNASSIGNED: Data from 216,625 log entries of five electronic exams, taken by a total of 1,241 veterinary medicine students in 2021 and 2022, were analyzed. Various statistical methods were employed to assess the correlations between the recorded parameters.
UNASSIGNED: The analysis revealed that different item formats require varying amounts of time. For instance, image-based question formats and Kprim necessitated more than 60 s per item, whereas one-best-answer multiple-choice questions (MCQs) and individual Key Feature items were effectively completed in less than 60 s. Furthermore, there was a positive correlation between character count and response time, suggesting that longer items require more time. A negative correlation could be verified for the parameters \"difficulty\" and \"discrimination index\" towards response time, indicating that more challenging items and those that are less able to differentiate between high- and low-performing students take longer to answer.
UNASSIGNED: The findings highlight the need for careful consideration of the ratio of item formats when defining time limits for exams. Regarding exam design, the literature mentions that time pressure is a critical factor, since it can negatively impact students\' exam performance and some students, such as those with disabilities, are particularly disadvantaged. Therefore, this study emphasizes finding the right time limits to provide sufficient time for answering questions and reducing time pressure. In the context of unsupervised online exams, the findings of this study support previous recommendations that implementation of a stringent time limit might be a useful strategy to reduce cheating.

Every individual experiences negative emotions, such as fear and anger, significantly influencing how external information is perceived and processed. With the gradual rise in brain-behavior relationship studies, analyses investigating individual differences in negative emotion processing and a more objective measure such as the response time (RT) remain unexplored. This study aims to address this gap by establishing that the individual differences in the speed of negative facial emotion discrimination can be predicted from whole-brain functional connectivity when participants were performing a face discrimination task. Employing the connectome predictive modeling (CPM) framework, we demonstrated this in the young healthy adult group from the Human Connectome Project-Young Adults (HCP-YA) dataset and the healthy group of the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) dataset. We identified distinct network contributions in the adult and adolescent predictive models. The highest represented brain networks involved in the adult model predictions included representations from the motor, visual association, salience, and medial frontal networks. Conversely, the adolescent predictive models showed substantial contributions from the cerebellum-frontoparietal network interactions. Finally, we observed that despite the successful within-dataset prediction in healthy adults and adolescents, the predictive models failed in the cross-dataset generalization. In conclusion, our study shows that individual differences in the speed of emotional facial discrimination can be predicted in healthy adults and adolescent samples using their functional connectivity during negative facial emotion processing. Future research is needed in the derivation of more generalizable models.

Higher intraindividual variability (IIV) of response times is consistently noted in children with attention-deficit/hyperactivity disorder (ADHD). The current study investigated whether an ex-Gaussian estimate of IIV in children ages 6-13 years-old could differentiate between children with and without ADHD. Children completed a computerized go/no-go task to estimate trial-by-trial IIV and a continuous performance test (CPT) to estimate inattention and hyperactivity/impulsivity. Parents completed questionnaires assessing inattention and hyperactive/impulsive behaviors. IIV, commission errors, and attention problems as rated by parents were significantly greater in the ADHD group. Groups did not differ on errors of omission, but IIV was predictive of omission errors and parent ratings of inattention and hyperactivity/impulsivity. IIV predicted group membership (ADHD vs Control) whereas errors of omission did not. However, IIV did not improve diagnostic accuracy when parent ratings were used, such that parent ratings were superior at determining diagnosis. Current results support the use of IIV, based on the ex-Gaussian approach, as an objective measure of attention problems over omission errors on sustained attention CPT-type tasks. Additionally, while parent ratings of attention impairment remain the best predictor of ADHD diagnostic status, IIV may be helpful in determining when further assessment is required in the absence of those ratings.