Conditional Dependencies Between Response Time and Item Discrimination: An Item-Level Meta-Analysis.

Item Discrimination, Difficulty, and Response Time for 4-Option Multiple Choice Questions Versus 3-Option Multiple Choice Questions.

BackgroundNew South Wales (NSW), Australia has a network of multirole retrieval physician staffed helicopter emergency medical services (HEMS) with seven bases servicing a jurisdiction with population concentrated along the eastern seaboard. The aim of this study was to estimate optimal HEMS base locations within NSW using advanced mathematical modelling techniques.MethodsWe used high resolution census population data for NSW from 2011 which divides the state into areas containing 200–800 people. Optimal HEMS base locations were estimated using the maximal covering location problem facility location optimization model and the average response time model, exploring the number of bases needed to cover various fractions of the population for a 45 min response time threshold or minimizing the overall average response time to all persons, both in green field scenarios and conditioning on the current base structure. We also developed a hybrid mathematical model where average response time was optimised based on minimum population coverage thresholds.ResultsSeven bases could cover 98% of the population within 45mins when optimised for coverage or reach the entire population of the state within an average of 21mins if optimised for response time. Given the existing bases, adding two bases could either increase the 45 min coverage from 91% to 97% or decrease the average response time from 21mins to 19mins. Adding a single specialist prehospital rapid response HEMS to the area of greatest population concentration decreased the average state wide response time by 4mins. The optimum seven base hybrid model that was able to cover 97.75% of the population within 45mins, and all of the population in an average response time of 18 mins included the rapid response HEMS model.ConclusionsHEMS base locations can be optimised based on either percentage of the population covered, or average response time to the entire population. We have also demonstrated a hybrid technique that optimizes response time for a given number of bases and minimum defined threshold of population coverage. Addition of specialized rapid response HEMS services to a system of multirole retrieval HEMS may reduce overall average response times by improving access in large urban areas.

In this paper, we develop analytical models and evaluate the performance of RAID5 disk arrays in normal mode (all disks operational), in degraded mode (one disk broken, rebuild not started) and in rebuild mode (one disk broken, rebuild started but not finished). Models for estimating rebuild time under the assumption that user requests get priority over rebuild activity have also been developed. Separate models were developed for cached and uncached disk controllers. Particular emphasis is on the performance of cached arrays, where the caches are built of Non-Volatile memory and support write caching in addition to read caching. Using these models, we evaluate the performance of arrayed and unarrayed disk subsystems when driven by a database workload such as those seen on systems running any of several popular database managers. In particular, we assume single-block accesses, flat device skew and little seek affinity. With the above assumptions, we find six significant results. First, in normal mode, we find there is no difference in performance between subsystems built out of either small arrays or large arrays as long as the total number of disks used is the same. Second, we find that if our goal is to minimize the average response time of a subsystem in degraded and rebuild modes, it is better to use small arrays rather than large arrays in the subsystem. Third, we find the counter-intuitive result that if our goal is to minimize the average response time of requests to any one array in the subsystem, it is better to use large arrays than small arrays in the subsystem. We call this the best worst-case phenomenon. Fourth, we find that when no caching is used in the disk controller, subsystems built out of arrays have a normal mode performance that is significantly worse than an equivalent unarrayed subsystem built of the same drives. For the specific drive, controller, workload and system parameters we used for our calculations, we find that, without a cache in the controller and operating at typical I/O rates, the normal mode response time of a subsystem built out of arrays is 50% higher than that of an unarrayed subsystem. In rebuild mode, we find that a subsystem built out of arrays can have anywhere from 100% to 200% higher average response time than an equivalent unarrayed subsystem. Out fifth result is that, with cached controllers, the performance differences between arrayed and equivalent unarrayed subsystems shrink considerably. We find that the normal mode response time in a subsystem built out of arrays is only 4.1% higher than that of an equivalent unarrayed system. In degraded (rebuild) mode, a subsystem built out of small arrays has a response time 11% (13%) higher and a subsystem built out of large arrays has a response time 15% (19%) higher than an unarrayed subsystem. Our sixth and last result is that cached arrays have significantly better response times and throughputs than equivalent uncached arrays. For one workload, a cached array with good hit ratios had 5 times the throughout and 10 to 40 times lower response times than the equivalent uncached array. With poor hit ratios, the cached array is still a factor of 2 better in throughput and a factor of 4 to 10 better in response time for this same workload. We conclude that 3 design decisions are important when designing disk subsystems built out of RAID level 5 arrays. First, it is important that disk subsystems built out of arrays have disk controllers with caches, in particular Non-Volatile caches that cache writes in addition to reads. Second, if one were trying to minimize the worst response time seen by any user, one would choose disk array subsystems built out of large RAID level 5 arrays because of the best worst-case phenomenon. Third, if average subsystem response time is the most important design metric, the subsystem should be built out of small RAID level 5 arrays.

Introduction: Interfacility transport (IFT) is a complex component of out-of-hospital Emergency Medical Services (EMS) which provides care and transport to patients who need transfer from one facility to another. The aim of IFT is that patients receive the care they need in a time-efficient and safe manner, and it helps in maintaining high practice standards and reduces financial burden. The delay in response time of interfacility transfer for life-saving cases may result in adverse health outcomes. Objective: The study aimed to identify possible causes of delay in ambulance response time for three life-saving categories (CVA, Stroke & RTA) and to reduce the average response time by applying improvement initiatives. Methods: This was a QI study. It was carried out by EMS at Eastern Health Cluster (EHC) Saudi Arabia from August 2022 till June 2023. In first phase retrospective analysis was conducted for the pre-intervention period (Jan 2022 to Aug 2022) to assess average response time for interfacility transfer of life-saving cases and to identify possible causes of delay through root cause analysis (RCA). In the second phase (Sep 2022 to April 2023) IHI improvement approach was applied to improve the efficiency of interfacility transfer for life-saving cases. Results: The retrospective data analysis highlighted the average response time for three lifesaving categories was 17 minutes. Certain interventions were applied and there was a significant reduction in average response time for three life-saving categories from 17 minutes to 9 minutes. The results of the paired-t test indicated that there was a significant difference t (7) = 15.3, p < .001 between before intervention response time (M = 17, SD = 1.7, n=8) and after intervention response time (M = 9.1, SD = 2, n=8). The highest average response was in December 2022 and average number of cases increased from 39 to 42 cases per month. Conclusion: The findings of this showed significant improvement in response time by merely introducing soft interventions e.g., EMS smart solution, and without acquiring additional staff or required ambulances. The rural region in EHC is vast with less concentration of stations, ambulances, and staff. Use of technology and staff resistance were also challenges. A fully functional EMS headquarter with resource control center may further improve EMS functioning. Hiring staff, acquiring ambulances & staff development is necessary to maintain and enhance gains. These findings can be replicated to improve IFT response time in different settings.

Epoch versus impulse models in the analysis of parametric fMRI studies

There recently have been many studies examining conditional dependence between response accuracy and response times in cognitive tests. While most previous research has focused on revealing a general pattern of conditional dependence for all respondents and items, it is plausible that the pattern may vary across respondents and items. In this paper, we attend to its potential heterogeneity and examine the item and person specificities involved in the conditional dependence between item responses and response times. To this end, we use a latent space item response theory (LSIRT) approach with an interaction map that visualizes conditional dependence in response data in the form of item-respondent interactions. We incorporate response time information into the interaction map by applying LSIRT models to slow and fast item responses. Through empirical illustrations with three cognitive test datasets, we confirm the presence and patterns of conditional dependence between item responses and response times, a result consistent with previous studies. Our results further illustrate the heterogeneity in the conditional dependence across respondents, which provides insights into understanding individuals' underlying item-solving processes in cognitive tests. Some practical implications of the results and the use of interaction maps in cognitive tests are discussed.

The focus of this paper is on the empirical relationship between item difficulty and item discrimination. Two studies—an empirical investigation and a simulation study—were conducted to examine the association between item difficulty and item discrimination under classical test theory and item response theory (IRT), and the effects of the association on various quantities of interest. Results from the empirical investigation show that item difficulty and item discrimination are negatively correlated under classical test theory, mostly negatively correlated under the two‐parameter logistic model, and mostly positively correlated under the three‐parameter logistic model; the magnitude of the correlation varied over the different data sets. Results from the simulation study reveal that a failure to incorporate the correlation between item difficulty and item discrimination in IRT simulations may provide the investigator with inaccurate values of important quantities of interest, and may lead to incorrect operational decisions. Implications to practice and future directions are discussed.

Statistical modeling of emergency medical services’ response and rescue times to road traffic crashes in the Kingdom of Saudi Arabia

This study proposes explanatory cognitive diagnostic model (CDM) jointly incorporating responses and response times (RTs) with the inclusion of item covariates related to both item responses and RTs. The joint modeling of item responses and RTs intends to provide more information for cognitive diagnosis while item covariates can be used to predict item parameters when item calibration is not feasible in diagnostic assessments or item parameter estimation errors could be too large due to small sample sizes for calibration. In addition, the inclusion of the item covariates allows the evaluation of cognitive theories underlying the test design in item development. Model parameter estimation is explored using the Bayesian Markov chain Monte Carlo (MCMC) method. A Monte Carlo simulation study is conducted to examine the parameter recovery of the proposed model under different simulated conditions in comparison to alternative competing models. Further, the application of the proposed model is illustrated using the Programme for International Student Assessment (PISA) 2012 problem‐solving items modeling both item response and RT data. The study results indicate that model parameters can be well recovered using the MCMC algorithm and the explanatory CDM jointly incorporating item responses and RTs with item covariates holds promising applications in digital‐based diagnostic assessments.

Psychologists theorize that cognitive reasoning involves two distinct processes: System 1, which is rapid, unconscious, and contextual, and System 2, which is slow, logical, and rational. According to the literature, diagnostic errors arise primarily from System 1 reasoning, and therefore they are associated with rapid diagnosis. This study tested whether accuracy is associated with shorter or longer times to diagnosis. Immediately after the 2010 administration of the Medical Council of Canada Qualifying Examination (MCCQE) Part II at three test centers, the authors recruited participants, who read and diagnosed a series of 25 written cases of varying difficulty. The authors computed accuracy and response time (RT) for each case. Seventy-five Canadian medical graduates (of 95 potential participants) participated. The overall correlation between RT and accuracy was -0.54; accuracy, then, was strongly associated with more rapid RT. This negative relationship with RT held for 23 of 25 cases individually and overall when the authors controlled for participants' knowledge, as judged by their MCCQE Part I and II scores. For 19 of 25 cases, accuracy on each case was positively related to experience with that specific diagnosis. A participant's performance on the test overall was significantly correlated with his or her performance on both the MCCQE Part I and II. These results are inconsistent with clinical reasoning models that presume that System 1 reasoning is necessarily more error prone than System 2. These results suggest instead that rapid diagnosis is accurate and relates to other measures of competence.

Completing test items under multiple speed conditions avoids the performance measure being confounded with individual differences in the speed-accuracy compromise, and offers insights into the response process, that is, how response time relates to the probability of a correct response. This relation is traditionally represented by two conceptually different functions: the speed-accuracy trade-off function (SATF) across conditions relating the condition average response time to the condition average of accuracy, and the conditional accuracy function (CAF) within a condition describing accuracy conditional on response time. Using a generalized linear mixed modelling approach, we propose an item response modelling framework that is suitable for item response and response time data from experimental speed conditions. The proposed SATF and CAF model accommodates response time effects between conditions (i.e., person and item SATF slope) and within conditions (i.e., residual CAF slopes), captures person and item differences in these effects, and is suitable for measures with a strong speed component. Moreover, for a single condition a CAF model is proposed distinguishing person, item and residual CAF. The properties of the models are illustrated with an empirical example.

문항반응이론은 문항의 난이도와 변별도가 검사를 치른 집단에 무관하게 항상 일정하며, 학생들이 매번 다른 검사를 치른다고 해도 자신의 고유한 능력점수를 받도록 하기위한 문항분석방법이다. 본 연구에서는 2000년부터 2009년까지 최근 10년 동안 대학수학능력시험에 출제되었던 통계영역 문제에 대하여 문항반응이론을 통한 분석을 실시하고 문항변별도와 문항난이도 등에 대해서 알아보았다. 그 결과 거의 60%의 문항이 어려운 문항으로 나타났다. 그러나 문항변별도는 비교적 양호한 것으로 판명되었다. Item response theory provides a fixed results about students, regardless of the item difficulty and discrimina-tion and it is also a kind of item analysis methods which provides the same proper competence scores to students in spite of them taking different test repeatedly. In this paper, we researched item difficulty and item discrimina-tion and analyzed items in the national academic aptitude test which were given from 2000 to 2009 in the past 10 years through item response theory, especially, in connection with given items about statistical unit. As a result, we found that about 60 percents of the items were too difficult for high school students to solve, however, item discrimination proved to be great.

Modulating operating room music volume with the CanaryBox: A quality improvement initiative to improve anesthesia clinicians’ response times to alarms to improve quality of anesthetic care

Wrong-way driving (WWD) can result in severe crashes. By responding quickly to WWD dispatch calls, law enforcement officers (LEOs) could stop the wrong-way vehicle before a crash occurs. This paper analyzed law enforcement (LE) response times to WWD dispatch calls in Florida between January 2003 and April 2018 to determine significant effects. The average LE response time was much lower for 2013 onward than before 2013. Average response time was lower during nighttime and in urban areas and was higher for county roads and toll roads. Two ordinal logit models were also developed. These models found that dispatch calls closer to regional traffic management centers or rest areas, in urban areas, or on state roads or local roads typically had lower response times than calls not in these locations. In addition, WWD dispatch calls on toll roads had lower response times than calls on non-toll limited access facilities. Intelligent transportation system (ITS) WWD countermeasures with flashing signs, detection devices, cameras, and direct communication with traffic management centers also help LEOs respond quickly to detected WWD events and more accurately identify the vehicle’s location. As of June 2018, these technologies located at 70 toll road exit ramps in Florida have prompted 307 wrong-way drivers to turn around, possibly preventing nine crashes and saving LEOs over 116 h. The results of this research can help identify locations where increasing LEO presence or installing ITS WWD countermeasure technologies could help reduce WWD response time and WWD crashes, potentially saving lives.

The path computation element (PCE) framework provides functions and protocol extensions to address the computation of paths that span multiple routing areas or administrative domains in support of traffic engineering (TE) in multi-protocol label switching (MPLS) or generalized MPLS (GMPLS) networks. A PCE node typically resides at the domain or area border and is capable of computing optimal/diverse TE label switched paths (LSP) paths, and of providing dynamic inter-layer resource optimization (e.g. between optical, and packet layers) for the network's primary and backup capacity. Requests for path computation of an inter-area or domain TE LSP can be performed by either using a centralized PCE instance present within a domain that has TE visibility over all of the other areas/domains, or can be shared among multiple PCEs - one responsible for each domain-in a distributive way. In the latter case, a PCE-based path computation, consists of relying on more than one PCE to compute the overall end-to-end path. When a PCE is not able to compute the full end-to-end path, a decision has to be made to select and forward the computation request to a downstream PCE node. The downstream PCE node selection process is crucial in the amount of overall time taken to compute the full end-to-end path. Typically, routing information - e.g. reachability to the destination announced by area border routers or autonomous system border routers - is used to generate a set of candidate PCEs that are capable of processing further the path computation request. However, among the set of candidate PCE(s), the decision to elect a certain PCE and forward the path computation request can affect significantly the overall end-to-end path computation response time, and hence the over-all time to signal the inter-area(domain) TE LSP. There are a number of schemes that can be considered to elect a preferred PCE from a set of candidates; in this paper, we present three: a selection scheme using round-robin scheduling, a least-response delay selection, and an adaptive approach based on the individual path computation response times received from each of the candidate PCEs. The first scheme assumes that requests will be locally distributed in a round-robin fashion among a number of PCEs that are capable of progressing the path computation process. In this scheme, requests from a certain source can be assumed to be locally distributed evenly among the available candidate PCEs. This scheme, however, does not guarantee a global request balancing among the all candidate PCEs, and hence, can lead to some PCEs being overloaded with large queue of requests leading to increased delay in the overall path computation response. The second scheme assumes that the request originator preserves some performance measure; for example an average path computation response time for each of the candidate PCEs. The requestor then would always pick the PCE with the least response time. This scheme will achieve relatively better load request load balancing among PCEs, however, it might slightly overload some PCEs over others due to always selecting the PCE with the lowest response time for all the local requests. The third scheme assumes that an average response time is preserved for each of the candidate PCEs and requests arriving at the source are partitioned among the candidate PCEs depending on the ratio of the average response times recorded. We believe this scheme results in an improved load balancing of the path computation requests among the candidate PCEs, and hence minimizing the overall path computation time of the inter-area or domain LSPs.