Alternative Training Techniques Research Articles

Knee Arthroscopy: A Simulation Demonstrating the Imperial Knee Arthroscopy Cognitive Task Analysis (IKACTA) Tool.

Virtual reality and cadaveric simulations are expensive and not readily accessible1. Innovative and accessible training adjuncts are required to help meet training needs. Cognitive task analysis (CTA) has been used extensively to train pilots and surgeons in other surgical specialties2-6. However, the use of CTA tools within orthopaedics is in its infancy. Arthroscopic procedures are minimally invasive and require a different skill set compared with open surgery. Residents often feel poorly prepared to perform this in the operating room because of the steep learning curve associated with acquiring basic arthroscopic skills. We designed the Imperial Knee Arthroscopy Cognitive Task Analysis (IKACTA) tool, which is, to our knowledge, the first CTA tool described in the orthopaedic literature, demonstrating significant objective benefits in training novices to perform diagnostic knee arthroscopy. The IKACTA tool, which is the combination of the written description of the phases below and the videos (with superimposed audio recordings) of each phase, utilizes simultaneous written and audiovisual modalities to teach diagnostic knee arthroscopy. The procedure was divided into 7 phases: (1) operating room and patient setup, (2) preparation and draping, (3) anterolateral portal placement, (4) examination of the patellofemoral joint and the lateral gutter, (5) examination of the medial compartment and anteromedial portal placement, (6) examination of the intercondylar notch and the lateral compartment, and (7) postoperative care and rehabilitation.For each phase, there are sections on the technical steps, cognitive decision-making behind each technical step, and potential errors and solutions. Video clips recorded by an expert surgeon in the operating room specific to each phase and audio voice recordings explaining each phase superimposed on the video clips were combined with the written information to design the IKACTA tool. Not applicable. This learning tool allows a trainee to learn each technical step, the cognitive decision-making underpinning each step, and potential errors and solutions relevant to each phase of the procedure. Furthermore, the learner can use written and audiovisual modalities simultaneously to learn this technique by reading the written component of the tool first and then watching the relevant video clips with the audio recordings for each phase of the procedure. Alternative training techniques currently include the traditional apprenticeship model, which is becoming increasingly insufficient in the current environment of reduced training hours7,8. Adjuncts to this model are essential to help meet training needs. The IKACTA tool has demonstrated significant objective benefits for novice trainees to learn diagnostic knee arthroscopy9. The idea behind this learning tool is for the trainee surgeon to use this tool independent of the trainer, prior to attending the operating room. The tool provides trainees with knowledge and cognitive understanding of the procedural steps before they perform this procedure on patients. They are aware of potential errors and methods to avoid or overcome these errors. We believe that this tool will reduce the initial difficult phase of the learning curve for junior residents and, therefore, will improve training efficiency in the operating room.

Effect of Resident Involvement on Operative Time and Operating Room Staffing Costs

The operating room (OR) is a major driver of hospital costs; therefore, operative time is an expensive resource. The training of surgical residents must include time spent in the OR, but that experience comes with a cost to the surgeon and hospital. The objective of this article is to determine the effect of surgical resident involvement in the OR on operative time and subsequent hospital labor costs. The Kruskal-Wallis statistical test is used to determine whether or not there is a difference in operative times between 2 groups of cases (with residents and without residents). This difference leads to an increased cost in associated hospital labor costs for the group with the longer operative time. Cases were performed at Greenville Memorial Hospital. Greenville Memorial Hospital is part of the larger healthcare system, Greenville Health System, located in Greenville, SC and is a level 1 trauma center with up to 33 staffed ORs. A total of 84,997 cases were performed at the partnering hospital between January 1st, 2011 and July 31st, 2015. Cases were only chosen for analysis if there was only one CPT code associated with the case and there were more than 5 observations for each group being studied. This article presents a comprehensive retrospective analysis of 29,134 cases covering 246 procedures. The analysis shows that 45 procedures took significantly longer with a resident present in the room. The average increase in operative time was 4.8 minutes and the cost per minute of extra operative time was determined to be $9.57 per minute. OR labor costs at the partnering hospital was found to be $2,257,433, or $492,889 per year. Knowing the affect on operative time and OR costs allows managers to make smart decisions when considering alternative educational and training techniques. In addition, knowing the connection between residents in the room and surgical duration could help provide better estimates of surgical time in the future and increase the predictability of procedure duration.

Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization

Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN) has the advantages of high fault-tolerance, strong nonlinear mapping and learning ability, which provides an effective method for the daily runoff forecasting. However, its training has certain drawbacks such as time-consuming, slow learning speed and easily falling into local optimum, which cannot be ignored in the real world application. In order to overcome the disadvantages of ANN model, the artificial neural network model based on quantum-behaved particle swarm optimization (QPSO), ANN-QPSO for short, is presented for the daily runoff forecasting in this paper, where QPSO was employed to select the synaptic weights and thresholds of ANN, while ANN was used for the prediction. The proposed model can combine the advantages of both QPSO and ANN to enhance the generalization performance of the forecasting model. The methodology is assessed by using the daily runoff data of Hongjiadu reservoir in southeast Guizhou province of China from 2006 to 2014. The results demonstrate that the proposed approach achieves much better forecast accuracy than the basic ANN model, and the QPSO algorithm is an alternative training technique for the ANN parameters selection.

Reducing Anxiety: Training Attentional Disengagement from Threat Using a Modified Spatial Cueing Task

It is well established that trait anxious individuals have an attentional bias for threatening information and stimuli, as demonstrated through both the dot probe and cueing paradigms. In recent efforts to reduce attentional biases and minimize anxiety symptomatology, variations of the dot probe task have been used to train attention away from threat. However, under this paradigm, it is uncertain what the underlying mechanism for attentional bias is. Is anxiety being perpetuated by a shift in attention to threat or does it result from one’s inhibited ability to disengage from threat once it is attended to? The purpose of the present study was to train attention away from threat using a cueing task, thus isolating attentional biases to difficulties in disengaging from threat. The goal of this task was to train attention by manipulating the contingency between the location of a target and a threatening or affectively neutral pictorial cue. Eighty-nine trait anxious college students participated in this study. It was hypothesized that participants in training groups would be able to disengage from threat faster and subsequently show a greater decrease in state anxiety when compared to controls. However, the data failed to support the primary hypotheses of this study. Interestingly, none of the groups showed an attentional threat bias at pretest, which makes it difficult to evaluate the efficacy of the training task. Future research should continue to focus on developing alternative training techniques that can target the mechanism(s) responsible for attentional biases in anxious individuals.

A hybrid method of artificial neural networks and simulated annealing in monitoring auto-correlated multi-attribute processes

The quality characteristics of both manufacturing and service industries include not only the variables but the attributes as well. While a substantial research have been performed on auto-correlated variables, little attempt has been fulfilled for auto-correlated attributes. Ignoring the imbedded autocorrelation structure in constructing control charts cause not only the in-control run length to decrease, but also the false alarms to increase. To overcome these shortcomings, in this research, an autoregressive vector first models the autocorrelation structure of the process data. Then, a modified Elman neural network is developed to generate simulated data using the ARTA algorithm. Next, a new methodology based upon the modified Elman neural network capabilities is developed to not only monitor the process, but also to detect the cause of process deterioration. In this network, instead of the back propagation, a simulated annealing approach is proposed as an alternative training technique that is able to search globally. At the end, some simulation experiments are performed and the performance of the proposed methodology with the ones of existing control charting methods is compared. The results of the comparison study are encouraging.

The Effectiveness of Alternative Training Techniques on Analytical Procedures Performance*

This study examines the effectiveness of alternative techniques on improving analytical procedures performance. To account for the sequential and iterative nature of complex analytical procedures, we used two different knowledge acquisition mechanisms (worked-out and solving training) to develop specific tools that have not been used in prior audit research. In addition, we combined these mechanisms with different levels of self-explanation (with self-explanation or without self-explanation). Participants were provided with no (baseline group) or six techniques that included outcome feedback, example, solving, self-explanation, with self-explanation and solving with self-explanation. Thereafter, participants completed a final analytical procedures case that had an error seeded in the financial statements. Results indicate that worked-out example or problem solving training combined with self-explanation outperformed all other groups. Additional analyses provide insight on the performance gains of the various techniques in the initial hypothesis generation and subsequent information search and evaluation phases during analytical procedures. Our findings contribute to the literature in auditing and psychology by showing that combinations of techniques are required for designing effective for complex diagnostic decision making like analytical procedures.

The Effectiveness of Alternative Training Techniques on Analytical Procedures Performance

This study examines the effectiveness of alternative techniques on improving analytical procedures performance. To account for the sequential and iterative nature of complex analytical procedures, we used two different knowledge acquisition mechanisms (worked-out and solving training) to develop specific tools that have not been used in prior audit research. In addition, we combined these mechanisms with different levels of self-explanation (with self-explanation or without self-explanation). Participants were provided with no (baseline group) or six techniques that included outcome feedback, example, solving, self-explanation, with self-explanation and solving with self-explanation. Thereafter, participants completed a final analytical procedures case that had an error seeded in the financial statements. Results indicate that worked-out example or problem solving training combined with self-explanation outperformed all other groups. Additional analyses provide insight on the performance gains of the various techniques in the initial hypothesis generation and subsequent information search and evaluation phases during analytical procedures. Our findings contribute to the literature in auditing and psychology by showing that combinations of techniques are required for designing effective for complex diagnostic decision making like analytical procedures.

COMPUTER-BASED AND INSTRUCTOR-LED INJURY PREVENTION TRAINING FOR BOARD AND CARE STAFF

Board and care owners and administrators must find new and innovative ways to meet the training needs of those on their staffs. The authors investigated computer-based instruction as a way to teach injury prevention techniques to these care providers. A computer-based (CB) training program was used that had been adapted from a print-based/instructor-led (IL) program. This article discusses the effectiveness of the CB training as compared to the effectiveness of the traditional IL format. The CB and IL versions of a prevention training program were presented to board and care staff during five sessions. The 78 participants were randomly assigned to either the CB or IL groups. Both the CB and IL groups significantly increased their knowledge scores from pretest to posttest. The CB participants reported that they enjoyed the training and had no difficulty using the computers, even though some of them had never used a computer previously for any activity. Computer-based training can be an effective alternative training technique for this population.

Committee neural networks for porosity and permeability prediction from well logs

ABSTRACTNeural computing has moved beyond simple demonstration to more significant applications. Encouraged by recent developments in artificial neural network (ANN) modelling techniques, we have developed committee machine (CM) networks for converting well logs to porosity and permeability, and have applied the networks to real well data from the North Sea.Simple three‐layer back‐propagation ANNs constitute the blocks of a modular system where the porosity ANN uses sonic, density and resistivity logs for input. The permeability ANN is slightly more complex, with four inputs (density, gamma ray, neutron porosity and sonic). The optimum size of the hidden layer, the number of training data required, and alternative training techniques have been investigated using synthetic logs. For both networks an optimal number of neurons in the hidden layer is in the range 8–10. With a lower number of hidden units the network fails to represent the problem, and for higher complexity overfitting becomes a problem when data are noisy. A sufficient number of training samples for the porosity ANN is around 150, while the permeability ANN requires twice as many in order to keep network errors well below the errors in core data. For the porosity ANN the overtraining strategy is the suitable technique for bias reduction and an unconstrained optimal linear combination (OLC) is the best method of combining the CM output. For permeability, on the other hand, the combination of overtraining and OLC does not work. Error reduction by validation, simple averaging combined with range‐splitting provides the required accuracy. The accuracy of the resulting CM is restricted only by the accuracy of the real data. The ANN approach is shown to be superior to multiple linear regression techniques even with minor non‐linearity in the background model.

A Comparison of Computer-based and Instructor-led Training for Long-term Care Staff

Nursing facility administrators must find new and innovative ways to meet the training needs of their staff. The authors faced the challenge of teaching nursing facility staff about fire safety using computer-based training that had been adapted from a print-based/instructor-led (IL) program. This article discusses the effectiveness of the computer-based (CB) training as compared to the effectiveness of the traditional IL format. The CB and IL versions of a fire safety training program were presented to staff of a nursing facility. The 141 participants were randomly assigned to the CB, IL, or control groups. The control group completed a pretest and posttest, but received no training until the study was completed. Both the CB and IL groups significantly increased their knowledge scores from pretest to posttest and both significantly outperformed the control group. How the participants responded to the CB training was also important. Staff reported they enjoyed the CB training and had no difficulty using the computers. Computer-based training can be an effective alternative training technique for this population.

Reliable classification using neural networks: a genetic algorithm and backpropagation comparison

Although, the genetic algorithm (GA) has been shown to be a superior neural network (NN) training method on computer-generated problems, its performance — on real world classification data sets is untested. To gain confidence that this alternative training technique is suitable for classification problems, a collection of 10 benchmark real world data sets were used in an extensive Monte Carlo study that compares backpropagation (BP) with the GA for NN training. We find that the GA reliably outperforms the commonly used BP algorithm as an alternative NN training technique. While this does not prove that the GA will always dominate BP, this demonstrated reliability with real world problems enables managers to use NNs trained with GAs as decision support tools with a greater degree of confidence.

Beyond Backpropagation

The vast majority of neural network research relies on a gradient algorithm, typically a variation of backpropagation, to obtain the weights of the model. Because of the enigmatic nature of complex nonlinear optimization problems, such as training artificial neural networks, this technique has often produced inconsistent and unpredictable results. To go beyond backpropagation’s typical selection of local solutions, simulated annealing is suggested as an alternative training technique that will search globally. In this research, backpropagation will be directly compared with this global search technique via an intensive Monte Carlo study on seven test functions

Selected alternative training techniques in HRD

AbstractOver the years a number of training techniques and procedures have been developed that are not part of the mainstream but are believed by some to have utility for organizations trying to enhance human performance. This article discusses four of these alternative techniques—subliminal self‐help, mental imagery and practice, meditation, and Neurolinguistic Programming (NLP)—and examines the contributions of each from a scientific perspective. With the exception of mental practice, there is a paucity of data to demonstrate convincingly whether these alternative techniques promote or enhance individual or organizational effectiveness. It is concluded that effective professional practice depends on scientifically derived research results.

Invited reaction: Selected alternative training techniques in HRD

Invited reaction: Selected alternative training techniques in HRD

Improving performance on the WCST: variations on the original procedure

There have been several studies on WCST training in schizophrenia. However, it is still unclear what aspects of the test schizophrenic patients find most difficult: are they failing because of impaired attention, memory or planning, to name a few possibilities? To address this, seven different versions of the WCST were developed, including a range of alternative stimuli and training techniques. The subjects were 24 schizophrenics and 24 elderly controls, divided into eight groups of three pairs (one group merely repeating the standard version), matched on their poor performance of the WCST (<3 categories). Results on retesting with the standard WCST after carrying out a modified version, suggest levels of test performance are not fixed, and that modifications which promote planning, pacing and error detection are the most successful in improving WCST score, especially in schizophrenics. Additionally, performance was improved in controls, but not schizophrenic patients by increasing the verbal information contained in the task. The results indicate that WCST deficits in elderly controls are due to limited capacity, whereas in patient with schizophrenia, the problem may be due to the impaired monitoring of responses.

Advanced Instructional Provisions and Automated Performance Measurement

An advanced simulation research system is being developed to support experiments oriented toward quantifying the effect on transfer of training of alternative training and simulation techniques. Included in the system is sophisticated training research software which automates most of the functions traditionally performed by flight simulator instructors or operators. Also under development are techniques for automatically assessing pilot proficiency in the simulator and in the operational aircraft. Recent feasibility studies in performance measurement resulted in identification of necessary flight variables for assessing proficiency on two undergraduate pilot training maneuvers; established alternative approaches to developing measurement techniques on a broader scale; applied existing technology to develop an in-flight data acquisition system for the T-37 aircraft; and raised (or, more correctly, reraised) issues regarding standardization in instruction and rating procedures.

The Training Program as a Correctional Change Agent

Training programs are frequently called upon to be the in struments of institutional change-i.e., to move the institution toward a more rehabilitative posture-and institutional admin istrators typically turn to treatment personnel in the institution or persons allied with treatment personnel from outside the in stitution to make up their training staffs. The problems inherent in such a training-staff model are discussed with regard both to the goals typically set for training and to the means established to achieve these goals. The article suggests an alternative staff model and alternative training techniques oriented toward greater confrontation with one's working self and the inmate role. It also discusses techniques for effecting staff cooperation and program acceptance.