Existing research comparing error management (a strategy focusing on increasing the positive and decreasing the negative consequences of errors) to error prevention (a strategy focusing on working faultlessly), has identified error management as beneficial for multiple outcomes. Yet, due to various methodological limitations, it is unclear whether the effects previously found are due to error prevention, error management, or both. We examine this in an experimental study with a 2 (error prevention: yes vs. no) × 2 (error management: yes vs. no) factorial design. Error prevention had negative effects on cognition and adaptive transfer performance. Error management alleviated worry and boosted one's perceived self-efficacy. Overall, the results show that error prevention and error management have unique outcomes on negative affect, self-efficacy, cognition, and performance.
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http://dx.doi.org/10.1080/00224545.2016.1270891 | DOI Listing |
Cureus
December 2024
Acute Medicine, Portsmouth Hospitals University NHS Trust, Portsmouth, GBR.
Cardiology, a high-acuity medical specialty, has traditionally emphasised technical expertise, often overshadowing the critical role of non-technical skills (NTS). This imbalance stems from the historical focus on procedural competence and clinical knowledge in cardiology training and practice, leaving a significant gap in the development of crucial interpersonal and cognitive abilities. However, emerging evidence highlights the significant impact of NTS on patient outcomes, team dynamics, and overall healthcare efficiency.
View Article and Find Full Text PDFIndian J Nucl Med
November 2024
Department of Nuclear Medicine and Molecular Imaging, Homi Bhabha Cancer Hospital and Mahamana Pandit Madan Mohan Malaviya Cancer Centre, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Varanasi, India.
Background: With the increasing number of oncology cases and a parallel surge in chemotherapeutic drugs for treatment, the treating physicians conducts nephrotoxicity evaluation to provide a personalized dosing strategy. Of the various tests available, glomerular filtration rate (GFR) under gamma camera with help of Gates method has gained importance, being a good index of overall kidney functions. In addition to this, there has been an alternate and old method for GFR estimation: plasma sampling.
View Article and Find Full Text PDFMethodsX
June 2025
Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111 Indonesia.
This research introduces the Generalized Extreme Value Mixture Autoregressive (GEVMAR) model as an innovative approach for examining non-standard actuarial datasets within general insurance. Information concerning claim reserves often reveals notable volatility and multimodal distributions, attributes that standard models, including previous method such as the Gaussian Mixture Autoregressive (GMAR) model and other autoregressive methodologies, find problematic to manage effectively. The GEVMAR model integrates the Generalized Extreme Value (GEV) distribution alongside Bayesian estimation techniques, augmented by a modified Signal-to-Noise Ratio (SNR) metric to improve predictive accuracy.
View Article and Find Full Text PDFClin Neuropsychol
January 2025
Department of Psychiatry, Brown University Health, Providence, RI, USA.
Older adults with cognitive impairment are at risk of medication-taking errors. This study assessed the impact of providing medication adherence feedback to cognitively impaired older adults. Forty participants with mild cognitive impairment or mild dementia had their medication adherence electronically monitored for 8 weeks.
View Article and Find Full Text PDFBMC Psychiatry
January 2025
Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Background: Mental disorders are increasingly prevalent, leading to increased medical expenditures. To refine the reimbursement of medical costs for inpatients with mental disorders by health insurance, an accurate prediction model is essential. Per-diem payment is a common internationally implemented payment method for medical insurance of inpatients with mental disorders, necessitating the exploration of advanced machine learning methods for predicting the average daily hospitalization costs (ADHC) based on the characteristics of inpatients with mental disorders.
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