Omission Errors Research Articles

43 Chronic Neurobehavioral and Cognitive Symptoms in Combat Deployed Military Service Personnel With and Without History of Blast-Related Mild Traumatic Brain Injury

Objective:Investigate the relationship of chronic neurobehavioral and cognitive symptoms in military personnel with history of blast-related mild TBI and compare to a well-matched group of combat-deployed controls.Participants and Methods:274 participants (mean age=34 years; mean education=14.75 years; 91.2% male) enrolled in the EVOLVE longitudinal study of combat-deployed military personnel were subdivided into those with history of blast TBI (n=165) and controls without history of blast exposure and TBI (n=109). As part of a larger study, we conducted a sub-analysis of 5-year follow up data. We focused on group differences (Mann-Whitney U) and correlational relationships between self-report neurobehavioral symptoms via the Frontal Systems Behavior Scale (FrSBE) and cognitive performances on measures of attention, working memory, processing speed, and executive functioning including D-KEFS Color Word Interference (CWI), Trailmaking A and B, and the Conners Continuous Performance Test (CPT).Results:The Blast TBI group reported higher levels of neurobehavioral symptoms on the FrSBE (p<.001), including domains of apathy (p<.001), disinhibition (p<.001), and executive dysfunction (p<.001), compared to Controls. On cognitive measures, group differences were observed on CWI Inhibition/Switching (p=.008), Trails B time (p=.010), and CPT commission errors (p=.014), such that the Blast TBI group performed worse than Controls. No significant group differences were observed for CPT omission errors or CPT hit rate (p’s>.05). After adjustment for multiple comparisons, greater FrSBE apathy correlated with slower performance on Trails A for Blast TBI (r=0.22, p=.014) but not Controls. Apathy endorsement was not significantly related to CPT omission errors for either group (p’s>.05). Higher endorsement of disinhibition symptoms was associated with worse performance on CWI Inhibition (Blast TBI: r=-0.19, p=.036; Controls: r=-0.28, p=.012) and Inhibition/Switching (Blast TBI: r=-0.23, p=.010; Controls: r=-0.29, p=.010) conditions for both groups, whereas only the Blast TBI group showed significant relationships between disinhibition symptoms and Trails B-A time (r=0.20, p=.025) and CPT commission errors (r=.18, p=.038). Higher endorsement of executive dysfunction correlated with poorer performance for Trails B-A for both groups (Blast TBI: r=.24, p=.009; Controls: r=.24, p=.030).Conclusions:Our findings reveal that at 5-year follow up, military personnel with history of blast-related mild TBI reported significantly greater neurobehavioral symptoms and performed lower on standardized measures of executive functioning, relative to combat-deployed controls without TBI or blast exposure. Significant relationships between neurobehavioral symptoms and cognitive performance were present in both groups. However, these relationships were more pronounced in the Blast TBI group, including greater apathy associated with slower visual tracking as well as greater endorsement of disinhibition associated with set-switching. Objective measures of response inhibition were related to disinhibition endorsement for both groups, though impulsive errors were more pronounced for the Blast TBI group. Our results suggest chronic cognitive and neurobehavioral symptoms are present in military personnel with history of blast TBI exposure, and also discrepant from a well-matched control group of combat deployed military personnel. Future studies of this population should explore models to predict cognitive performance from neurobehavioral symptoms in military personnel, as this could inform treatment approaches for those at greatest risk of cognitive change.

68 The Impact of Pain Catastrophizing on Neuropsychological Performance in Youth with Persistent Post Concussive Symptoms

Objective:Patients with persistent post-concussion symptoms (PPCS) experience prolonged recovery (e.g., headache, fatigue, or dizziness) lasting >2 months post injury. These symptoms are thought to be maintained by several biopsychosocial factors including dysregulated stress responses, such as pain catastrophizing, that may drive behavioral avoidance and contribute to mood symptoms and cognitive difficulties. Conditions with similar symptomatology to PPCS (e.g., anxiety disorders, somatosensory disorders, chronic pain, etc.) also exhibit maladaptive thought patterns like pain catastrophizing as well as decrements in certain aspects of cognitive performance; however little is known about how pain catastrophizing might relate to neuropsychological performance in youth with PPCS. Therefore, the purpose of this study was to examine the relationship between pain catastrophizing and neuropsychological performance in youth participants with PPCS.Participants and Methods:A prospective case-control study design was used to examine 29 participants between the ages of 13 to 23. Participants were divided into two groups: 1) patients with PPCS (2-16 months post-injury; n = 15) and 2) age-matched, non-injured controls (n = 14). Participants completed the Pain Catastrophizing Scale (PCS) to determine degree of catastrophic thinking related to pain experience and the Beck Depression Inventory (BDI). Neuropsychological performance was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and a modified version of the Paced Auditory Serial Addition Test (PASAT) where performance was evaluated by total correct and error type (i.e., commission and omission) across 5 trials. ANCOVA was used to compare group differences in pain catastrophizing and neuropsychological tests scores while controlling for age and linear regressions examined the relationship between PCS total score and each neuropsychological test score while controlling for level of depression.Results:Overall, the PPCS group reported significantly higher levels of pain catastrophizing on the PCS compared to the control group (p < 0.01). For neuropsychological performance, the PPCS group scored significantly lower than the control group on List Learning (p < 0.01), Semantic Fluency (p < 0.05), and List Recall (p < 0.01) on the RBANS and made significantly higher omission errors (but not commission) on the PASAT(p <.01). Higher pain catastrophizing was also associated with poorer neuropsychological performance on the exact same tasks the PPCS group performed worse than controls. There was no significant interaction by group in the impact of PCS scores on neurocognitive performance.Conclusions:Compared to controls, youth PPCS patients reported higher levels of pain catastrophizing. Additionally, pain catastrophizing was associated with poorer neuropsychological performance. These findings suggest that increased pain catastrophizing after head injury could contribute to poorer cognitive performance in youth. As such, interventions that target maladaptive cognitive coping styles like pain catastrophizing may be especially helpful for patients with PPCS.

Andrew Mayes’ expert analysis of a case of memory distortion

In this single-case study of memory distortion where Andrew Mayes was involved as an expert witness, a surgeon falsely recalled aspects of a patient safety adverse event in which he was involved, and where he was put through an investigatory process by a regulatory body. Andrew Mayes’ expert analysis of the role of memory distortion in this case resulted in the surgeon being exonerated.

Derivational Morpheme Errors in English: A Study of Libyan Students at Al-Mergib University

Proficiency in a language entail mastering components such as lexical nuances and grammatical rules. However, morphology often gets overlooked in university programs, underscoring the need for students to heighten their morphological awareness to refine their English writing. This research delves into the errors in using derivational morphemes by third- and fourth-year English major students from Libya. The objective is to identify, categorize, and understand the root causes of these errors. The study, involving ten participants from Al-Mergib University, used a qualitative research approach with essay writing as the primary data collection tool. The analysis uncovers various errors in derivational morpheme usage. Specifically, the study recorded 36 omission errors, 16 order errors, 6 incorrect formations, and 3 other errors. A striking observation was that errors predominantly centered on suffixes over prefixes. The most frequent mistakes arose in the transformation of adjectives to adverbs and verbs to nouns, encompassing omissions, additions, misordering, and misformations. For educators, this study underscores the importance of understanding student challenges with derivational morphemes, especially related to verbs and nouns. It advocates for a pedagogical approach that includes group discussions analyzing relevant texts. By highlighting these errors, the study offers valuable insights into areas where Libyan students require focused attention in English morphology.

Towards tree-based systems disturbance monitoring of tropical mosaic landscape using a time series ensemble learning approach

Tree clearing and degradation inside and outside forest ecosystems in Africa are important contributors to global carbon budget and emissions. Part of the uncertainties in emission estimates, among other things, is related to the non-inclusion of disturbances across all tree-based systems and limitations in the capacity of existing methodologies to detect subtle changes or degradation. Here, we present a tree-based system (TBS) disturbance monitoring approach in a mosaic landscape in East Africa. For this purpose, high- and low-intensity disturbance detection time series algorithms, namely Breaks for Additive Season and Trend monitor (BFASTmonitor), Continuous Degradation Detection (CODED), and Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr), were used. These algorithms were tested in three approaches based on reference canopy cover change data from repeated airborne laser scanning. Data processing was performed using Collection 2 Landsat (5, 7, and 8) time series in Google Earth Engine. The approaches were based on testing the accuracy of 1) the algorithms' magnitude of change outputs using multispectral information (i.e., several vegetation indices and bands); 2) multispectral ensemble learning approach applied at each of the three algorithms; and 3) combining the multiple algorithms in an ensemble learning approach. Our results showed an improvement in the accuracy of detecting a TBS disturbance from approach 1 to 3. The best result (44% omission error and 31% commission error against reference airborne laser scanning data) was obtained when BFASTmonitor and CODED were combined in an ensemble learning approach. The shortwave infrared bands were more important for detecting TBS disturbance than green, red, and near-infrared bands. The application of the best model in TBS disturbance monitoring in the region revealed that disturbances were more dominant outside forest ecosystems than inside. Improved disturbance monitoring in TBS can contribute to more accurate emission estimates in Africa and elsewhere in the tropics.

Comparison and Assessment of Different Land Cover Datasets on the Cropland in Northeast China

The provision of precise and dependable information regarding the extent and distribution of cropland is imperative for the evaluation of food security, agricultural planning, and resource management. Cropland is an important component of land cover type and is offered in multiple existing global/regional land cover products. However, global-scale accuracy evaluation may not be representative of class-specific or local-area accuracy, such as in Northeast China, which is an important grain-producing region of China and has various types of cultivated land (e.g., wheat, rice) and diverse terrains. It poses a great challenge in generating precise cropland classification by automated mapping. Thus, it is indispensable to evaluate the accuracy and reliability of these various land cover datasets before using them. In this study, we collected thirteen sets of global or national-scale land cover datasets. Through the visual interpretation of high-resolution images, ground “truth” samples were collected to evaluate the data accuracy across Northeast China. The overall accuracy (OA) evaluation results in Phase-2020 show that CLCD has the highest value with 0.914, followed by GlobeLand30 (0.906), GLC_FCS30 (0.902), and Esri (0.896) for cropland classification in Northeast China. CGLS-LC100 has the lowest OA (0.710). For the commission and omission errors of six datasets in Phase-2020, CGLS-LC100 has an obvious overestimation (larger commission error), while the two national-scale datasets (CLCD and CLUDs) perform relatively better. In terms of spatial consistency, high spatial agreement among the nine Phase-2015 datasets or in the six Phase-2020 datasets could be discovered in traditional agricultural regions like the Sanjiang–Songnen–Liaohe Plain, and low agreement is found in the transition areas of mountains (hills) and plains with the mixed landscape of forest (grassland) and farmland. In the aspect of comparison pairwise data, CLCD is in good agreement with GLC_FCS30, GlobeLand30, and Esri, while CGLS-LC100 is in the poorest agreement with any other dataset. The comparison and evaluation results are expected to provide a reference on which aspects and to what extent these land cover products may be consistent and guide the cropland data product selection for Northeast China.

Development of audio-guided deep breathing and auditory Go/No-Go task on evaluating its impact on the wellness of young adults: a pilot study.

Numerous studies indicate that deep breathing (DB) enhances wellbeing. Multiple deep breathing methods exist, but few employ audio to reach similar results. This study developed audio-guided DB and evaluated its immediate impacts on healthy population via self-created auditory Go/No-Go task, tidal volume changes, and psychological measures. Audio-guided DB with natural sounds to guide the DB was developed. Meanwhile, audio-based Go/No-Go paradigm with Arduino was built to measure the attention level. Thirty-two healthy young adults (n=32) were recruited. Psychological questionnaires (Rosenberg's Self-Esteem Scale (RSES), Cognitive and Affective Mindfulness Scale-Revised (CAMS-R), Perceived Stress Scale (PSS)), objective measurements with tidal volume and attention level with auditory Go/No-Go task were conducted before and after 5 min of DB. Results showed a significant increment in tidal volume and task reaction time from baseline (p=0.003 and p=0.033, respectively). Significant correlations were acquired between (1) task accuracy with commission error (r=-0.905), (2) CAMS-R with task accuracy (r=-0.425), commission error (r=0.53), omission error (r=0.395) and PSS (r=-0.477), and (3) RSES with task reaction time (r=-0.47), task accuracy (r=-0.362), PSS (r=-0.552) and CAMS-R (r=0.591). This pilot study suggests a link between it and young adults' wellbeing and proposes auditory Go/No-Go task for assessing attention across various groups while maintaining physical and mental wellness.

The Wetland Intrinsic Potential tool: mapping wetland intrinsic potential through machine learning of multi-scale remote sensing proxies of wetland indicators

Abstract. Accurate, unbiased wetland inventories are critical to monitor and protect wetlands from future harm or land conversion. However, most wetland inventories are constructed through manual image interpretation or automated classification of multi-band imagery and are biased towards wetlands that are easy to directly detect in aerial and satellite imagery. Wetlands that are obscured by forest canopy, that occur ephemerally, and that have no visible standing water are, therefore, often missing from wetland maps. To aid in the detection of these cryptic wetlands, we developed the Wetland Intrinsic Potential (WIP) tool, based on a wetland-indicator framework commonly used on the ground to detect wetlands through the presence of hydrophytic vegetation, hydrology, and hydric soils. Our tool uses a random forest model with spatially explicit input variables that represent all three wetland indicators, including novel multi-scale topographic indicators that represent the processes that drive wetland formation, to derive a map of wetland probability. With the ability to include multi-scale topographic indicators that help identify cryptic wetlands, the WIP tool can identify areas conducive to wetland formation while providing a flexible approach that can be adapted to diverse landscapes. For a study area in the Hoh River watershed in western Washington, USA, classification of the output probability with a threshold of 0.5 provided an overall accuracy of 91.97 %. Compared to the National Wetlands Inventory, the classified WIP tool output identified over 2 times the wetland area and reduced errors of omission from 47.5 % to 14.1 % but increased errors of commission from 1.9 % to 10.5 %. The WIP tool is implemented as an ArcGIS toolbox using a combination of R and Python scripts.

Evaluation of Spectral Indices for Mapping Burned Areas using Unsupervised Classification in Different Ecosystems using Spectral Indices from Sentinel-2 images

Fire is one of the natural agents that has the greatest impact on the terrestrial ecosystem and plays an important ecological function in a huge portion of the Earth's surface. Remote sensing is an important source for mapping and monitoring forests as well as environmental damage to the landscape caused by fire.To estimate the severity of the fire, the temporal distinction between the spectral indices before and after the fire is critical. This study examines the performance of spectral indices derived from Sentinel-2 Multi Spectral Instrument (MSI) bands for the detection of fire-affected areas in forest fires in regions with different ecosystems located in Brazil, the United States, and Portugal. Separability Index (M) and the Reed-Xiaoli Detection (RXD) Anomaly automatic classifier allowed us to assess the spectral separability and the thematic accuracy of the burned area for the different spectral indices. The analysis parameters were based on spatial dispersion with validation data, Commission Errors (CE), Omission Errors (OE), and Sorensen–Dice Coefficient (DC). The results indicated that the indices based exclusively on the longer shortwave infrared (SWIR1), shorter SWIR (SWIR2), and red-edge bands showed a high degree of separability and were more suitable for detecting the burned areas, although it was observed that the diversity of land use affects the performance of the indices. The evaluation of the performance of the spectral indices based on Sentinel-2 data was important in the analyzes of potentialities and limitations in the detection of burned areas in the face of the different global biomes.

Grammatical Errors of Google Translate in Translating Narrative Text in Indonesia to English

Many people have recently turned to machine translation to help them translate their documents. However, it is not widely used in the academic field, owing to worries about its trustworthiness in appropriately rendering the source text in the target language. This tool only translates on a word-by-word basis without considering the actual meaning of the text and grammar rules in English. It often applies to the Indonesian structure. So, when students are using Google Translate to translate a text, a new issue arises because it just transfers language without knowing the correct grammar. This research methodology is qualitative research with a content analysis method as the research design. From 5 narrative texts that have been translated by Google Translate from Indonesia to English found that there are two omission errors, four addition errors, six misinformation, and two misordering error and the most common error is misinformation. This study seeks to educate students about grammatical issues that can arise when using Google Translate, allowing them to avoid making the same mistakes and improve their knowledge.

Mapping 10 m monthly surface water dynamics in the Yangtze River basin from 2017 to 2020 using a robust ATMC algorithm

Surface water bodies are irreplaceable in the global climate and ecological system. However, quantifying the extent and change of these water bodies has been challenging due to environmental complexity, climatic diversity, and their highly dynamic nature in seasons and interannual periods. Most established remote-sensing water extraction methods involve a practical trade-off between spatial and temporal resolution. To remedy these deficiencies, we proposed a simple and robust Automatic Threshold–based Multi-source data Combination algorithm to improve the efficiency and accuracy of water mapping, achieving monthly stable periodic monitoring. The algorithm fully considered the principles and advantages of Sentinel-1/2 images for water detection and performed adaptive optimal threshold calculation based on the Edge Otsu algorithm. A new product of Surface Water Dynamics in the Yangtze River Basin was generated on a monthly temporal scale and 10-m spatial scale from 2017 to 2020 using the algorithm based on the Google Earth Engine platform. The confusion matrix-based metrics based on sample points were 95.85 % (producer's accuracy), 99.16 % (user's accuracy), 97.41 % (overall accuracy), 0.95 (Matthews correlation coefficient), and 0.95 (Kappa coefficient). Furthermore, the overall correct extraction rate of water bodies can reach 95.85 % and 91.87 % for small water bodies, with commission and omission errors of 0.04 % and 0.03 % at the sub-pixel scale, respectively. The distribution of water bodies within the basin is highly uneven, and their changes exhibit a distinct seasonal trend. The maximum inundation extent occurred in August 2020 (summer, 53,372.88 km2), while the minimum inundation extent was observed in November 2019 (winter, 42,491.74 km2). The successful application of the algorithm in the Yangtze River Basin established a good benchmark for surface water monitoring of multi-source satellite observations and showed great potential for global-scale applications. It will assist the monitoring and evaluation services of the UN Sustainable Development Goals and provide solutions to more global and regional issues.

B - 50 The Relationship between Objective and Subjective Measures of Attention.

The relationship between objective and subjective measures of attention is understudied. This study aimed to evaluate the convergence between commonly used measures in the assessment of attention-deficit hyperactivity disorder (ADHD) and to determine whether there is a significant difference between individuals with and without ADHD on subscales related to ADHD symptoms. This study involved an archival data set of Veterans (N = 504) who completed the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF) or MMPI-3, the Barkley's Adult ADHD Rating Scale or a local DSM-5/TR ADHD diagnostic interview, the Connor Continuous Performance Test 3 (CCPT-3) or the Integrated Visual & Auditory 2, and the Trail Making Test as part of a clinical assessment in a specialty ADHD clinic. Nonparametric comparisons between those with and without ADHD revealed no statistically significant differences across the cognitive measures. Self-reported symptoms of inattention (z = 12.07, p < 0.001, r = 0.57, large effect), hyperactivity (z = 4.70, p < 0.001, r = 0.22, small effect), and impulsivity (z = 5.52, p < 0.001, r = 0.26, small effect), significantly differentiated participants with and without ADHD. Pearson correlations between the two types of measures showed that omission errors on the CCPT-3 were significantly correlated to self-reported inattention symptoms (r = 0.15, p = 0.039). All other correlations between objective and subjective measures were not significant. These results suggest that neuropsychological measures are not strongly correlated to self-reported symptoms of inattention, and as such may be less sensitive to ADHD.

Frequency of nursing student medication errors: A systematic review

Background: Health promotion and patient safety are the main targets of the healthcare provision by the National Health Systems. As for the nursing profession, nursing students make medication errors during clinical interventions, which could be a potential danger to patient safety. The investigation of the frequency of nursing student medication errors, as well as the frequency of each type of nursing student medication errors. Method and Material: A systematic review of the literature was conducted on the electronic database "PubMed" with the keywords: "medication error", "prescribing error", "drug error", "drug use error", "drug mistake", "wrong drug", "wrong dose", "administration error", "dispensing error", "incorrect drug", "incorrect dose", "inappropriate prescribing", "inappropriate medication", "transcription error", "nursing student", "nursing trainee" and on the Greek electronic database IATROTEK-online with the keywords: "medication errors" and "nursing students", without time limit for the publication of scientific papers. On PubMed, the keywords were searched in the title and abstract of the studies. Studies were excluded if they were not published in English and Greek language, were conducted on animals, and were case studies, editorials, and letters to the editor. Results: Of the 47 scientific papers retrieved, 6 were included in the systematic review. A total of 1,904 nursing student medication errors were recorded by nursing students. The majority of errors were: 1) wrong dose form (330,17%), 2) omission error (313, 16.4%), and 3) wrong time (259, 13.6%). Conclusions: The frequency of nursing student medication errors is high. The safe administration of medications is an important skill that nursing students should learn. medication administration.

Predicting Continental Scale Malaria With Land Surface Water Predictors Based on Malaria Dispersal Mechanisms and High-Resolution Earth Observation Data.

Despite malaria prevalence being linked to surface water through vector breeding, spatial malaria predictors representing surface water often predict malaria poorly. Furthermore, precipitation, which precursors surface water, often performs better. Our goal is to determine whether novel surface water exposure indices that take malaria dispersal mechanisms into account, derived from new high-resolution surface water data, can be stronger predictors of malaria prevalence compared to precipitation. One hundred eighty candidate predictors were created by combining three surface water malaria exposures from high-accuracy and resolution (5m resolution, overall accuracy 96%, Kappa Coefficient 0.89, Commission and Omission error 3% and 13%, respectively) water maps of East Africa. Through variable contribution analysis a subset of strong predictors was selected and used as input for Boosted Regression Tree models. We benchmarked the performance and Relative Contribution of this set of novel predictors to models using precipitation instead of surface water predictors, alternative lower resolution predictors, and simpler surface water predictors used in previous studies. The predictive performance of the novel indices rivaled or surpassed that of precipitation predictors. The novel indices substantially improved performance over the identical set of predictors derived from the lower resolution Joint Research Center surface water data set (+10% R 2, +17% Relative Contribution) and over the set of simpler predictors (+18% R 2, +30% Relative Contribution). Surface water derived indices can be strong predictors of malaria, if the spatial resolution is sufficiently high to detect small waterbodies and dispersal mechanisms of malaria related to surface water in human and vector water exposure assessment are incorporated.

Monitoring selective logging intensities in central Africa with sentinel-1: A canopy disturbance experiment

Forest degradation is a major threat to tropical forests, and effective monitoring using remotely sensed data is subject to significant challenges. In particular, consistent methods for detecting subtle changes in the forest canopy structure caused by selective logging are lacking. Here, using a unique dataset collected in southeastern Cameroon, covering over 22,000 ha of monthly harvesting areas, >6000 locations of harvested trees, and an independent canopy gap dataset developed from an uninhabited aerial vehicle flight (UAV; RGB camera) of approximately 1500 ha, a new method was designed to monitor canopy disturbance and logging intensity in Central Africa. Using Sentinel-1 synthetic aperture radar (SAR) data, the method was conceptualised using a two-step, two-scale approach, which better matched logging practices. First, (non-)harvesting activity areas were identified using textural indices at a spatial resolution of 300 m (step 1), and within these harvesting activity areas, canopy gaps were detected at a resolution of 10 m (step 2). Both steps were based on monthly differences in the Sentinel-1 SAR time series computed using the average of the 12 months preceding and the average of the three months following the month of interest. This method identified harvesting activity areas (step 1 at 300 m resolution) of over 12,004 km2 with high accuracy (omission and commission errors for both classes ≤0.05) and, within them, detected canopy gaps (step 2 at 10 m resolution) with a global accuracy of 0.89. Although some canopy gaps were subject to omission and commission errors (0.39 and 0.05, respectively), this method yielded better results than other available approaches. Compared to the UAV canopy gaps, this method detected most of the small gaps (≤ 500 m2), which represent 80% of all disturbed areas, whereas other available approaches missed at least 70% of these and consequently missed most of the disturbance events occurring in a selectively logged forest. Furthermore, the predictions were correlated with logging intensity, i.e., the number of trees and volume cut per hectare, which are two important criteria for assessing the sustainability of logging activities. This two-step two-scale method for short-term, monthly monitoring of logging disturbances and intensity has strong practical implications for forest administration and certification bodies in Central Africa.

Lexical Errors in Third Language Acquisition: A Comparative Analysis of Writing Performance in Rabat-Sale and Souss-Massa Regions of Morocco

The comparative study explores the phenomenon of lexical cross-linguistic influence on high school students' writing performance within two distinct regions of Morocco: Rabat-Sale and Souss-Massa. Using James Taxonomy as a framework, written narratives produced by 200 students from both regions were analyzed focusing on their lexical choices and the occurrence of lexical errors. Distinct patterns of lexical errors, including Calque, Prefix, Over-inclusion, Borrowing, and Omission errors, were identified in students' narratives. The results revealed notable differences in lexical errors between the Souss-Massa and Rabat-Sale regions. Souss-Massa students exhibited higher frequencies of Calque (25), Prefix (20), Over-inclusion (35), and Omission (28), indicating a stronger influence from other linguistic sources and potential issues with precision and clarity in their writing. In contrast, Rabat-Sale students showed a higher frequency of Borrowing (28), suggesting a greater tendency to incorporate foreign language elements into their writing. These results have implications for tailored language education and curriculum development in Morocco and emphasize the importance of considering regional linguistic nuances.

ERROR ANALYSIS ON STUDENTS WRITING TASK OF PERSONAL LETTERS

This study tries to classify and explain the many types of writing errors that were discovered in students' personal letters. The outcomes are ascertained by a qualitative data analysis employing the writing assignments of the students as the source of data. The Surface Strategy Taxonomy is used to pinpoint the errors. The researcher employs comparative analysis to divide the students into several skill levels when gathering data. The goal of this study is to identify mistakes committed by science-majoring eleventh-graders at SMAN 6 Kota Semarang, who number 36 students, in their personal letter assignment. The total of errors are 274 errors were classified in this study, with omission errors (f=131, 47.81%) as the most common. The second commonplace fault category was mis-formation errors (f=127, 46.35%). These categories were followed by addition error (f=9, 3.28%) and mis-order error (f=7, 2.55%). From the above result, we can conclude that the most prevalent mistakes among the learners is omission where they frequently eliminate sort of parts which needed in the full sentences due to the fact lack of English grammar knowledge.

Memory distrust and suggestibility: A registered report

AbstractPurposeThe study aimed to explore how memory distrust impacts two kinds of suggestibility: misinformation effect (Experiment 1) and interrogative suggestibility (Experiment 2). We verified whether recognizing discrepancies between personal memories and externally suggested information, along with certain individual differences, moderates the memory distrust‐suggestibility link.MethodsExperiment 1 (N = 306) followed a classic three‐step procedure with a discrepancy awareness test. In Experiment 2 (N = 316) a computerized version of the Gudjonsson Suggestibility Scale with a discrepancy awareness test was utilized. Both experiments manipulated the state of memory distrust and assessed participants' trait memory distrust, compliance, anxiety and self‐esteem.ResultsWhile the effect of memory deterioration due to misinformation and suggestions was replicated, the hypotheses regarding the relationship between memory distrust and suggestibility were not confirmed. However, for interrogative suggestibility, individuals induced to doubt their own memory were more susceptible to suggestions when recognizing inconsistencies between the story and suggestive questions.ConclusionsFrom the theoretical side, the concept of memory distrust appears to be useful to explain the reasons for succumbing to suggestions. However, our findings indicate that memory distrust toward committing omission errors and the experimentally induced state appear unrelated (misinformation effect paradigm) or marginally related (memory distrust state and interrogative suggestibility) to the tendency to yield to suggestions. This prompts further research using a different operationalisation of this construct.

Determining Medication Errors in an Adult Intensive Care Unit.

Research addressing the costs of Medication errors (MEs) is still scarce despite issues related to patient safety having significant economic and health impacts, making it imperative to analyze the costs and adverse events related to MEs for a better patient, professional, and institutional safety. To identify the number of medication errors and verify whether this number was associated with increased hospitalization costs for patients in an Intensive Care Unit (ICU). This retrospective cross-sectional cohort study evaluated secondary data from patients' electronic medical records to compile variables, create a model, and survey hospitalization costs. The statistical analysis included calculating medication error rates, descriptive analysis, and simple and multivariate regression. The omission error rate showed the highest number of errors per drug dose (59.8%) and total errors observed in the sample (55.31%), followed by the time error rate (26.97%; 24.95%). The omission error had the highest average when analyzing the entire hospitalization (170.40) and day of hospitalization (13.79). Hospitalization costs were significantly and positively correlated with scheduling errors, with an increase of BRL 121.92 (about USD $25.00) (95% CI 43.09; 200.74), and to prescription errors, with an increase of BRL 63.51 (about USD $3.00) (95% CI 29.93; 97.09). We observed an association between two types of medication errors and increased hospitalization costs in an adult ICU (scheduling and prescription errors).

How dual-task interference on word production is modulated by the timing of the secondary task: evidence from errors in people with aphasia

ABSTRACT Background Utterance production is affected under dual-task conditions, but so far studies have shown an impact either on lexical or on phonological processes, but not on both simultaneously. Aims In the present study, we aimed at investigating how interference on lexical and phonological encoding is modulated by the timing of the concurrent task and by the attentional requirement (divided vs focused attention). Methods Participants with aphasia (PWA) underwent a picture naming task and an auditory detection task, with auditory stimuli presented at different stimulus onset asynchronies (SOAs). The dual-task was performed under divided (Experiment 1) and focused attention (Experiment 2). Outcome & Results Omission errors increased under dual-task at early SOA only in Experiment 1 while phonological errors increased at later SOAs in both experiments. Conclusion These patterns of errors indicate that lexical and phonological processes are impacted under dual-task conditions, giving rise to an increase of specific types of errors at specific SOAs. They also show that very mild anomia may be more severe when assessed under dual-task conditions.