Error Categories Research Articles

ANALISIS KESALAHAN SISWA DALAM MENYELESAIKAN SOAL FUNGSI KOMPOSISI DITINJAU DARI PERBEDAAN GENDER

This study was conducted with the aim of identifying errors that occur when students solve composition function problems by paying attention to gender differences. The research subjects were four students of class XI, two male students and two female students. The research method used was descriptive qualitative. Tests and interviews were conducted to collect data. The data were then analyzed by examining the students' answer sheets and classifying the errors made into four types, namely concept, principle, fact, and operation errors. The findings showed that male students made more errors than female students, especially in the category of fact and operation errors. Fact errors made by male students are errors in writing arithmetic operation signs, this is because male students do not understand the concept of multiplication and addition operation signs. While the error in determining the final answer was caused by an error in the previous process, where male students could not do the calculation correctly so that they got the wrong final result. The conclusion of this study confirms that female students have an advantage in the ability to solve math problems compared to male students.

Error Analysis of English Narrative Essays of Secondary School Learners in Kirinyaga Central Sub-County, Kenya

The study examined the types of language errors made by learners in the English narrative essays. It also assessed the feedback techniques employed by teachers of English in the handling of language errors in the English narrative essays. A descriptive research design was employed to examine errors of English narrative essays of 181 form two learners obtained through stratified random sampling and simple random sampling techniques. The eight teachers of English selected through purposive and simple random sampling completed interview schedules. Qualitative data generated from the essays of the learners was analyzed using Error Analysis and Content Analysis. The findings reveal that a total of seven categories of language errors which include: word order, verb tense and form, subject – verb agreement, spelling, plurality, word choice, and capital letter errors were made by the learners in their English narrative essays. The most preferred corrective technique was to involve the learners actively by encouraging them to read extensively and write severally on various narratives in the target language. The findings will provide reliable feedback to teachers of English because it will guide on the development of teaching methods and evaluative measures to narrative essays. The study will also be useful to curriculum developers in designing materials for teaching English language in secondary schools. It will also be of invaluable benefit to learners of English as it will guide on the way narrative essays need to be written.

In vivo EPID-based daily treatment error identification for volumetric-modulated arc therapy in head and neck cancers with a hierarchical convolutional neural network: a feasibility study.

We proposed a deep learning approach to classify various error types in daily VMAT treatment of head and neck cancer patients based on EPID dosimetry, which could provide additional information to support clinical decisions for adaptive planning. 146 arcs from 42 head and neck patients were analyzed. Anatomical changes and setup errors were simulated in 17,820 EPID images of 99 arcs obtained from 30 patients using in-house software for model training, validation, and testing. Subsequently, 141 clinical EPID images from 47 arcs belonging to the remaining 12 patients were utilized for clinical testing. The hierarchical convolutional neural network (HCNN) model was trained to classify error types and magnitudes using EPID dose difference maps. Gamma analysis with 3%/2mm (dose difference/distance to agreement) criteria was also performed. The F1 score, a combination of precision and recall, was utilized to evaluate the performance of the HCNN model and gamma analysis. The adaptive fractioned doses were calculated to verify the HCNN classification results. For error type identification, the overall F1 score of the HCNN model was 0.99 and 0.91 for primary type and subtype identification, respectively. For error magnitude identification, the overall F1 score in the simulation dataset was 0.96 and 0.70 for the HCNN model and gamma analysis, respectively; while the overall F1 score in the clinical dataset was 0.79 and 0.20 for the HCNN model and gamma analysis, respectively. The HCNN model-based EPID dosimetry can identify changes in patient transmission doses and distinguish the treatment error category, which could potentially provide information for head and neck cancer treatment adaption.

Assessment of remote sensing in measuring soil parameters for precision tillage

Precision tillage (PT) is an innovative method that aims to take mechanical actions in the soil only where it is needed to curb the impact of heavy machinery usage on the soil. This research explores the use of remote sensing to measure relevant soil parameters to implement a PT strategy. This was achieved by combining traditional soil properties measurements and a non-contact approach based on taking hyperspectral camera (HSC) data in the field. Six methods were generated and divided into two sets to determine soil properties to make PT decisions. The first set includes mathematical functions that were generated from the ground true data (GTD). The second set includes functions that were generated from the remotely sensed HSC data and have a relationship with the methods in the first set. It was possible to tune the functions’ parameters to increase the accuracy. In addition, prediction error categories set at 5 % intervals were used to select the best method. The results show that a tuned method based on the GTD has an overall error below 5 %, and a tuned method based on HSC data has an overall error below 10 %.

Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy.

Background Errors in radiology reports may occur because of resident-to-attending discrepancies, speech recognition inaccuracies, and large workload. Large language models, such as GPT-4 (ChatGPT; OpenAI), may assist in generating reports. Purpose To assess effectiveness of GPT-4 in identifying common errors in radiology reports, focusing on performance, time, and cost-efficiency. Materials and Methods In this retrospective study, 200 radiology reports (radiography and cross-sectional imaging [CT and MRI]) were compiled between June 2023 and December 2023 at one institution. There were 150 errors from five common error categories (omission, insertion, spelling, side confusion, and other) intentionally inserted into 100 of the reports and used as the reference standard. Six radiologists (two senior radiologists, two attending physicians, and two residents) and GPT-4 were tasked with detecting these errors. Overall error detection performance, error detection in the five error categories, and reading time were assessed using Wald χ2 tests and paired-sample t tests. Results GPT-4 (detection rate, 82.7%;124 of 150; 95% CI: 75.8, 87.9) matched the average detection performance of radiologists independent of their experience (senior radiologists, 89.3% [134 of 150; 95% CI: 83.4, 93.3]; attending physicians, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; residents, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; P value range, .522-.99). One senior radiologist outperformed GPT-4 (detection rate, 94.7%; 142 of 150; 95% CI: 89.8, 97.3; P = .006). GPT-4 required less processing time per radiology report than the fastest human reader in the study (mean reading time, 3.5 seconds ± 0.5 [SD] vs 25.1 seconds ± 20.1, respectively; P < .001; Cohen d = -1.08). The use of GPT-4 resulted in lower mean correction cost per report than the most cost-efficient radiologist ($0.03 ± 0.01 vs $0.42 ± 0.41; P < .001; Cohen d = -1.12). Conclusion The radiology report error detection rate of GPT-4 was comparable with that of radiologists, potentially reducing work hours and cost. © RSNA, 2024 See also the editorial by Forman in this issue.

Surface Strategy Taxonomy in Errors Analysis: Made by the Students of Senior High School

This research was conducted to find out and analyze the grammatical errors in translating Indonesian procedure text into English. The aim of this research was to find out and analyze the errors and their types. This problem was categorized under grammatical errors using four types of Dullay’s Strategy Taxonomy, they are omission, addition, misformation, and misordering. The researcher used descriptive qualitative method. Generally, there are several techniques to collect data needed such as the researcher prepared two Indonesian procedure texts, then gave the texts to the students. The researcher used test technique to collect the data needed and the test was done for 60 minutes in class. The data were presented in the form of table and ordered based on each of the translation problems to make it easier for the analysis. The result of the data analysis reveals that the problems which occurred in the students’ worksheet are related to the types of grammatical errors (33,48 %) omission errors, (4,21 %) addition errors, (4,44%) misformation errors, (57,84%) misordering errors. The most identified error belongs to misordering error which is given the highest score to the total number of errors identified (57,84%) and the least identified one belongs to addition error category (4,21%). The fact indicates that there are some students who have adequate knowledge in translating the texts. In other words, there are also students who have problems in translating Indonesian procedure texts into English

Barriers to medication error reporting in a federally qualified health center

ObjectiveTo explore the National Coordinating Council for Medication Error Reporting and Prevention Categories of Errors health professionals are most likely to report and characterize what barriers to medication error reporting influence decisions to report and the extent they do so at a large federally qualified health center (FQHC). DesignProspective, cross-sectional, survey. Setting and participantsA total of 161 medical professionals at a large FQHC clinic with a small pharmacy team. Outcome measuresSurvey responses to explore respondent understanding of medication error categories and the influence of barriers to medication error reporting on their decision to report. ResultsThirty-six (22.4%) respondents completed the survey. Nearly 40% of respondents would not report a near-miss error and were influenced by workplace/environmental barriers significantly more than those who would report. Regardless of reporting experience or patient-care role, assessed barrier categories influence the decision to report similarly. ConclusionNear-miss medication errors are inconsistently reported. Efforts to improve reporting should emphasize addressing workplace/environmental barriers.

Identification of application and interpretation errors that can occur in pairwise meta-analyses in systematic reviews of interventions: a systematic review

ObjectivesTo generate a bank of items describing application and interpretation errors that can arise in pairwise meta-analyses in systematic reviews of interventions. Study Design and SettingMEDLINE, Embase, and Scopus were searched to identify studies describing types of errors in meta-analyses. Descriptions of errors and supporting quotes were extracted by multiple authors. Errors were reviewed at team meetings to determine if they should be excluded, reworded, or combined with other errors, and were categorized into broad categories of errors and subcategories within. ResultsFifty articles met our inclusion criteria, leading to the identification of 139 errors. We identified 25 errors covering data extraction/manipulation, 74 covering statistical analyses, and 40 covering interpretation. Many of the statistical analysis errors related to the meta-analysis model (eg, using a two-stage strategy to determine whether to select a fixed or random-effects model) and statistical heterogeneity (eg, not undertaking an assessment for statistical heterogeneity). ConclusionWe generated a comprehensive bank of possible errors that can arise in the application and interpretation of meta-analyses in systematic reviews of interventions. This item bank of errors provides the foundation for developing a checklist to help peer reviewers detect statistical errors.

Differences in prescribing errors between electronic prescribing and traditional prescribing among medical students: A randomized pilot study.

This randomized controlled pilot study aimed to assess the differences in the frequency, type and severity of prescribing errors made by medical students when assessed in an electronic (e-)prescribing system compared to a traditional prescribing method (e.g., typing out a prescription). Fourth year medical students in the period of 1 November to 31 July 2023, were asked to participate in this single-centre prospective, randomized, controlled intervention study. Participants performed a prescribing assessment in either an e-prescribing system (intervention group) or in a more traditional prescribing platform (control group). The prescriptions were checked for errors, graded and categorized. Differences in prescribing errors, error categories and severity were analysed. Out of 334 students, 84 participated in the study. Nearly all participants (98.8%) made 1 or more prescribing errors, primarily involving inadequate information errors. In the intervention group, more participants made prescribing errors involving the prescribed amount (71.4 vs. 19.0%; P< .01), but fewer involving administrative errors (2.4 vs. 19.0%; P= .03). Prescribing-method-specific errors were identified in 4.8 and 40.5% of the intervention and control group, respectively, with significant differences in overlapping errors as well. This pilot study shows the importance of training e-prescribing competencies in medical curricula, in addition to traditional prescribing methods. It identifies prescribing-method-specific prescribing errors and emphasizes the need for further research to define e-prescribing competencies. Additionally, the need for an accessible real-life-like e-prescribing environment tailored to educators and students is essential for effective learning and incorporation of e-prescribing into medical curricula.

MT error detection and correction by Chinese language learners

Abstract This article analyzes texts that have been generated by machine translation (MT) and post-edited by native English-speaking trainee translators (English&gt;Chinese) who are also Chinese language learners enrolled in a four-year undergraduate translation program. The project examines the work product of trainee translators to categorize 122 errors that are (un)noticed and (un)corrected by them. MT errors in the Accuracy category were best identified and corrected, followed by those in the Lexicon and Fluency categories. Trainee translators who were advanced language learners outperformed the intermediate group in MT error detection and correction, especially in the Lexicon category. This study sheds light upon the use of raw MT output as meaningful input for trainee translators who are in the process of learning Chinese. Its findings provide information regarding the type of exercises needed in language learning and translation training for students with different levels of language proficiency.

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN-LSTM model.

Skin cancer is a terrifying disorder that affects all individuals. Due to the significant increase in the rate of melanoma skin cancer, early detection of skin cancer is now more critical than ever before. Malignant melanoma is one of the most serious forms of skin cancer, and it is caused by abnormal melanocyte cell growth. In recent years, skin cancer predictive categorization has become more accurate and predictive due to multiple deep learning algorithms. Malignant melanoma is diagnosed using the Recurrent Convolution Neural Network-Long Short-Term Memory (RCNN-LSTM), which is one of the deep learning classification approaches. Using the International Skin Image Collection and the RCNN-LSTM, the data are categorized and analyzed to gain a better understanding of skin cancer. The method begins with data preprocessing, which prepares the dataset for classification. Additionally, the RCNN is employed to extract the features that are vital to the prediction process. The LSTM is accountable for the final step, classification. There are further factors to examine, such as the precision of 94.60%, the sensitivity of 95.67%, and the F1-score of 95.13%. Other benefits of the suggested study include shorter prediction durations of 95.314, 122.530, and 131.205 s and lower model loss of 0.25%, 0.19%, and 0.15% for input sizes 10, 15, and 20, respectively. Three datasets had a reduced categorization error of 5.11% and an accuracy of 95.42%. In comparison to previous approaches, the work discussed here produces superior outcomes. RESEARCH HIGHLIGHTS: Recurrent convolutional neural network (RCNN) deep learning approach for optimizing time prediction and error classification in early melanoma detection. It extracts a high number of specific features from the skin disease image, making the classification process easier and more accurate. To reduce classification errors in accurately detecting melanoma, context dependency is considered in this work. By accounting for context dependency, the deprivation state is avoided, preventing performance degradation in the model. To minimize melanoma detection model loss, a skin disease image augmentation or regularization process is performed in this work. This strategy improves the accuracy of the model when applied to fresh, previously unobserved data.

Grammatical versus Spelling Error Correction: An Investigation into the Responsiveness of Transformer-Based Language Models Using BART and MarianMT

Text continues to remain a relevant form of representation for information. Text documents are created either in digital native platforms or through the conversion of other media files such as images and speech. While the digital native text is invariably obtained through physical or virtual keyboards, technologies such as OCR and speech recognition are utilised to transform the images and speech signals into text content. All these variety of mechanisms of text generation also introduce errors into the captured text. This project aims at analysing different kinds of errors that occur in text documents. The work employs two of the advanced deep neural network-based language models, namely, BART and MarianMT, to rectify the anomalies present in the text. Transfer learning of these models with available dataset is performed to finetune their capacity for error correction. A comparative study is conducted to investigate the effectiveness of these models in handling each of the defined error categories. It is observed that while both models can bring down the erroneous sentences by 20+%, BART can handle spelling errors far better (24.6%) than grammatical errors (8.8%).

Revisiting the immunopathology of congenital disorders of glycosylation: an updated review.

Glycosylation is a critical post-translational modification that plays a pivotal role in several biological processes, such as the immune response. Alterations in glycosylation can modulate the course of various pathologies, such as the case of congenital disorders of glycosylation (CDG), a group of more than 160 rare and complex genetic diseases. Although the link between glycosylation and immune dysfunction has already been recognized, the immune involvement in most CDG remains largely unexplored and poorly understood. In this study, we provide an update on the immune dysfunction and clinical manifestations of the 12 CDG with major immune involvement, organized into 6 categories of inborn errors of immunity according to the International Union of Immunological Societies (IUIS). The immune involvement in phosphomannomutase 2 (PMM2)-CDG - the most frequent CDG - was comprehensively reviewed, highlighting a higher prevalence of immune issues during infancy and childhood and in R141H-bearing genotypes. Finally, using PMM2-CDG as a model, we point to links between abnormal glycosylation patterns in host cells and possibly favored interactions with microorganisms that may explain the higher susceptibility to infection. Further characterizing immunopathology and unusual host-pathogen adhesion in CDG can not only improve immunological standards of care but also pave the way for innovative preventive measures and targeted glycan-based therapies that may improve quality of life for people living with CDG.

A novel equation for the estimation of low-density lipoprotein cholesterol in the Saudi Arabian population: a derivation and validation study

Low-density lipoprotein cholesterol (LDL-C) is typically estimated by the Friedewald equation to guide atherosclerotic cardiovascular disease (ASCVD) management despite its flaws. Martin–Hopkins and Sampson-NIH equations were shown to outperform Friedewald’s in various populations. Our aim was to derive a novel equation for accurate LDL-C estimation in Saudi Arabians and to compare it to Friedewald, Martin–Hopkins and Sampson-NIH equations. This is a cross-sectional study on 2245 subjects who were allocated to 2 cohorts; a derivation (1) and a validation cohort (2). Cohort 1 was analyzed in a multiple regression model to derive an equation (equationD) for estimating LDL-C. The agreement between the measured (LDL-CDM) and calculated levels was tested by Bland–Altman analysis, and the biases by absolute error values. Validation of the derived equation was carried out across LDL-C and triglyceride (TG)-stratified groups. The mean LDL-CDM was 3.10 ± 1.07 and 3.09 ± 1.06 mmol/L in cohorts 1 and 2, respectively. The derived equation is: LDL-CD = 0.224 + (TC × 0.919) – (HDL-C × 0.904) – (TG × 0.236) – (age × 0.001) – 0.024. In cohort 2, the mean LDL-C (mmol/L) was estimated as 3.09 ± 1.06 by equationD, 2.85 ± 1.12 by Friedewald, 2.95 ± 1.09 by Martin–Hopkins, and 2.93 ± 1.11 by Sampson-NIH equations; statistically significant differences between direct and calculated LDL-C was observed with the later three equations (P < 0.001). Bland–Altman analysis showed the lowest bias (0.001 mmol/L) with equationD as compared to 0.24, 0.15, and 0.17 mmol/L with Friedewald, Martin–Hopkins, and Sampson-NIH equations, respectively. The absolute errors in all guideline-stratified LDL-C categories was the lowest with equationD, which also showed the best classifier of LDL-C according to guidelines. Moreover, equationD predicted LDL-C levels with the lowest error with TG levels up to 5.63 mmol/L. EquationD topped the other equations in estimating LDL-C in Saudi Arabians as it could permit better estimation when LDL-C is < 2.4 mmol/L, in familial hyperlipidemia, and in hypertriglyceridemia, which improves cardiovascular outcomes in high-risk patients. We recommend further research to validate equationD in a larger dataset and in other populations.

Errors on Sentence Structures Made by Students in Writing 2 and Writing 4 Classes

This study analyzed the types of errors on sentence structures by students in Writing 2 and Writing 4 classes in the English Department, Petra Christian University, and their similarities and differences. Eight categories of common errors on sentence structures proposed by Ho (2005) were used to analyze thedrafts. The types of common errors in this study were limited to four: Run-on Sentence, Fragmented Sentence, Inappropriate Subordinate Conjunction, and Misordering. The findings showed that both classes made all types of the errors and the most common error made was run-on sentences in the form of comma splice. Fragmented sentences with missing verb and subject were only found on writing 2 drafts while subordinating clauses in fragmented sentences were found on writing 4 drafts. Moreover, errors in relation to subordinating conjunction were more prominent in Writing 4. In conclusion, difficulties in utilizing conjunctions, and the different complexity of the drafts might affect the errors on sentence structures.

Analysis of Artifacts and Errors on Intraoral Phosphor Plate Radiographs: A Retrospective Study

Introduction. The advantages of phosphor plates (PPs), including their flexibility, thinness, and wireless connectivity, have contributed to their widespread use in dentistry alongside the advancement of digital radiography techniques. Continuous use of PPs and certain errors may lead to artifacts on the images. This retrospective study aimed to determine the frequency and types of intraoral PP artifacts.&#x0D; Methods. This study was conducted on 814 intraoral PP radiographs, including periapical and bitewing images. The errors and artifacts were classified into 4 main categories: technical errors, plate-related artifacts, scanner-induced artifacts, and ambient light artifacts.&#x0D; Results. A total of 656 periapical and 158 bitewing radiographs were examined. The most observed artifacts on the radiographs were plate-related artifacts, followed by technical errors (n=542, n=461, respectively). Within the category of plate-related artifacts, the most common artifacts were cracks or scratches (n=418, 77.1%), while within the category of technical errors, these were cone-cut errors (n=188, 40.7%). Parallel or zigzag radiopaque lines were the most common scanner-induced artifacts (n=313, 98.7%) and fading was the most common ambient light artefact (n=93, 49.2%). On more than half of the PPs, more than one artifact group was observed.&#x0D; Conclusions. Cone-cut, cracks/scratches, parallel zigzag radiopaque lines, and fading were common PP artifacts and errors in this study. The analysis revealed that over half of the PPs exhibited appearance of more than one artifact group. The high frequency of artifacts poses a risk of incorrect and incomplete diagnoses in radiographs, emphasizing the crucial importance of understanding the causes and developing effective prevention strategies for artifacts to enhance the reliability of diagnostic imaging and ensure the accuracy of patient assessments.

Impaired cognitive flexibility and disrupted cognitive cerebellum in degenerative cerebellar ataxias.

There is a clinically unmet need for a neuropsychological tool that reflects the pathophysiology of cognitive dysfunction in cerebellar degeneration. We investigated cognitive flexibility in degenerative cerebellar ataxia patients and aim to identify the pathophysiological correlates of cognitive dysfunction in relation to cerebellar cognitive circuits. We prospectively enrolled degenerative cerebellar ataxia patients with age-matched healthy controls who underwent 3 T 3D and resting-state functional MRI. All 56 participants were evaluated with the Scale for Assessment and Rating of Ataxia and neuropsychological tests including the Wisconsin Card Sorting Test, Trail Making Test, Montreal Cognitive Assessment and Mini-Mental State Examination. From MRI scans, we analysed the correlation of whole-brain volume and cortico-cerebellar functional connectivity with the Wisconsin Card Sorting Test performances. A total of 52 participants (29 ataxia patients and 23 healthy controls) were enrolled in this study. The Wisconsin Card Sorting Test scores (total error percentage, perseverative error percentage, non-perseverative error percentage and categories completed), Trail Making Test A and Montreal Cognitive Assessment were significantly impaired in ataxia patients (P < 0.05) compared to age-matched healthy controls. The Wisconsin Card Sorting Test error scores showed a significant correlation with the ataxia score (P < 0.05) controlling for age and sex. In volumetric analysis, the cerebellar right crus I, II, VIIb and VIII atrophy correlated with non-perseverative error percentage in the ataxia group. In functional connectivity analysis, the connectivity between crus I, II and VIIb of the cerebellum and bilateral superior parietal and superior temporal gyrus was significantly altered in ataxia patients. The functional connectivity between left crus II and VIIb of the cerebellum and dorsolateral prefrontal and superior frontal/parietal cortices showed a positive correlation with perseverative error percentage. The connectivity between left crus VIIb and pontine nucleus/middle cerebellar peduncle showed a significant negative correlation with non-perseverative error percentage in the ataxia group. The impaired cognitive flexibility represented by the Wisconsin Card Sorting Test was significantly impaired in degenerative cerebellar ataxia patients and correlated with disease severity. The Wisconsin Card Sorting Test performance reflects hypoactivity of the cognitive cerebellum and disrupted cortico-cerebellar connectivity in non-demented patients with degenerative cerebellar ataxia.

The effect of visitation restrictions on ED error.

EDs restricted visitors during the COVID-19 pandemic on the assumption that the risks of disease spread outweighed the psychological benefits of liberal visitation. But data suggest that beyond providing emotional support, family and caregivers can clarify history, improve patient monitoring, and advocate for patients-actions that can improve quality of care. Our objective was to assess whether removing visitors from the bedside contributed to errors in emergency care. We reviewed a database of medical errors covering visits from 11/15/17 to 7/30/22 at an urban, tertiary-care, academic ED for five types of error amenable to visitor intervention: inadequate history gathering, inadequate monitoring, falls, giving a medication to which a patient is allergic, and inappropriate medication dosing. These records were reviewed by two investigators to determine the likelihood visitor presence could have prevented the error. For those errors judged susceptible to visitor intercession, the number in each category was compared for the period before and after strict restrictions took effect. Our review found 27/781 (3.5%) errors in the pre-pandemic period and 27/568 (4.8%) errors in the pandemic period fell into one of these five categories (p = 0.29). Visitors prevented harm from reaching the patient in three of 27 pre-pandemic errors (11.1%), compared to 0 out of 27 peri-pandemic errors (p = 0.23). On review by two attendings, 17/24 (70.8%) errors that reached the patient in the pre-pandemic period were judged amenable to visitor intervention, compared to 25/27 (92.6%) in the pandemic period (p = 0.09). There were no statistically significant differences in the categories of error between the two groups; monitoring errors came the closest: 1/17 (5.9%) pre-COVID errors amenable to visitor intervention in these categories were monitoring related, whereas 7/25 (28.0%) post-COVID errors were (p = 0.16). While this study did not demonstrate a statistically significant difference in error between lenient and restrictive visitation eras, we did find multiple cases in the pre-COVID era in which family presence prevented error, and qualitative review of post-COVID errors suggested many could have been prevented by family presence. Larger trials are needed to determine how frequent and consequential such errors are and how to balance the public health imperative of curbing disease spread with the harm caused by restricting visitation.

The Effect of Direct and Indirect Written Corrective Feedback on Accuracy and Fluency of University Students' English Writing

This research aimed to determine the effect of WCF (Written Corrective Feedback), direct and indirect, on overall accuracy, error types, and overall accuracy and fluency in pre-test, post-test, and delayed post-test in rewritten text. The research questions examined the potential links between the direct &amp; indirect WFC, grammar &amp; non-grammar errors, and standpoint of feedback for accuracy and fluency. A total of 100 English as a Foreign Language (EFL) students from the University of Pahlawan Tuanku Tambusai in Indonesia were involved in the research. The findings revealed significant variations among the three experimental groups in the proportions of both effectively and unsuccessfully rectified errors. as well as the proportions of uncorrected and deleted errors in both grammar and non-grammar categories. The cohort that received the Direct WCF had the most significant enhancement in accuracy. This study showed that offering direct WCF (written corrective feedback) can enhance participants accuracy gradually over an extended period of time (long term). Indirect written corrective feedback (WCF) can enhance participants' fluency in terms of overall word count. while direct WCF can improve participants' fluency in terms of t-units over a longer period of time.

Type of COVID-19 Vaccine Administration Errors and Deviations at the Primary Health Care Vaccination Sites in the State of Qatar 2021-2022

Objective This research aimed to assess coronavirus disease 2019 (COVID-19) vaccine administration errors at Primary Health Care Centres (PHCC) in Qatar and provide corrective measures to enhance patient safety based on the Centre for Communicable Diseases recommendations. Methods A retrospective analysis was conducted on the electronic medical records of patients receiving COVID-19 vaccines at PHCC between 23 December 2020 and 31 December 2021. Demographic information, vaccine details, administration dates, lot numbers, and vaccination sites were extracted. A descriptive analysis was performed to calculate the percentages of common vaccine administration errors outlined by the CDC. Results PHCC administered 2,797,941 and 694,643 vaccine doses in 2021 and 2022, respectively. The primary error category was data quality, with the highest observed error being a discrepancy between vaccine expiration dates and actual dates (3.1% in December 2022). In 2021, all error percentages within data quality were below 0.37%. Notably, the most prevalent vaccine administration deviation in 2021 was interval rule 2, where the second dose was administered more than 42 days after the first dose. The highest deviation (1.59%) was reported in November. Conclusions Vaccination remains crucial for disease prevention, necessitating accurate vaccine administration processes. The study underscores the importance of effective implementation and monitoring of vaccination programmes. PHCC’s strategies, including ongoing training, adherence to guidelines, and a monitoring dashboard, contributed to better error understanding. This approach facilitated targeted training and communication efforts to enhance patient safety and minimise potential harm. The findings highlight the significance of continuous improvement in vaccine administration to ensure the success of public health interventions.