Research on calibration remains a popular line of inquiry. Calibration is the degree of fit between a person's judgment of performance and his or her actual performance. Given the continued interest in this topic, the questions posed in this article are fruitful directions to pursue to help address gaps in calibration research. In this article, we have identified six research directions that if productively pursued, could greatly expand our knowledge of calibration. The six research directions are: (a) what are the effects of varying the anchoring mechanisms from which calibration judgments are made, (b) how does calibration accuracy differ as a function of incentives and task authenticity, (c) how do students self-report the basis of their calibration judgments, (d) how do group interactions and social comparisons affect calibration accuracy, (e) what is the relation between absolute and relative accuracy, and (f) to what extent does calibration accuracy predict achievement? To help point the way to where we go from here in calibration research, we provide these research questions, propose research methods designed to address them, and identify prior, related studies that have shown promise in leading the way to fill these gaps in the literature.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3408109 | PMC |
| http://dx.doi.org/10.3389/fpsyg.2012.00229 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A simple algorithm to derive virtual non-contrast electron density from dual-energy computed tomography data for radiotherapy treatment planning.
Med Phys
January 2025
Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, Hokkaido, Japan.
Background: The use of iodinated contrast-enhancing agents in computed tomography (CT) improves the visualization of relevant structures for radiotherapy treatment planning (RTP). However, it can lead to dose calculation errors by incorrectly converting a CT number to electron density.
Purpose: This study aimed to propose an algorithm for deriving virtual non-contrast (VNC) electron density from dual-energy CT (DECT) data.
External validation of a multivariable prediction model for positive resection margins in breast-conserving surgery.
BMC Res Notes
January 2025
Department of Surgery, Department of Clinical Sciences, Division of Surgery, Skåne University Hospital, Lund University, Lund, Sweden.
Objectives: Positive resection margins after breast-conserving surgery (BCS) most often demands a repeat surgery. To preoperatively identify patients at risk of positive margins, a multivariable model has been developed that predicts positive margins after BCS with a high accuracy. This study aimed to externally validate this prediction model to explore its generalizability and assess if additional preoperatively available variables can further improve its predictive accuracy.
View Article and Find Full Text PDFPSMA PET/CT based multimodal deep learning model for accurate prediction of pelvic lymph-node metastases in prostate cancer patients identified as candidates for extended pelvic lymph node dissection by preoperative nomograms.
Eur J Nucl Med Mol Imaging
January 2025
Department of Nuclear Medicine, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, Hunan, 410008, P.R. China.
Purpose: To develop and validate a prostate-specific membrane antigen (PSMA) PET/CT based multimodal deep learning model for predicting pathological lymph node invasion (LNI) in prostate cancer (PCa) patients identified as candidates for extended pelvic lymph node dissection (ePLND) by preoperative nomograms.
Methods: [Ga]Ga-PSMA-617 PET/CT scan of 116 eligible PCa patients (82 in the training cohort and 34 in the test cohort) who underwent radical prostatectomy with ePLND were analyzed in our study. The Med3D deep learning network was utilized to extract discriminative features from the entire prostate volume of interest on the PET/CT images.
Development and validation of a prognostic nomogram for predicting of patients with acute sedative-hypnotic overdose admitted to the intensive care unit.
Sci Rep
January 2025
Emergency Medicine Laboratory and the Department of Emergency, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China.
To develop and evaluate a predictive model for intensive care unit (ICU) admission among patients with acute sedative-hypnotic overdose. We conducted a retrospective analysis of patients admitted to the emergency department of West China Hospital, Sichuan University, between October 11, 2009, and December 31, 2023. Patients were divided into ICU and non-ICU groups based on admission criteria including the need for blood purification therapy, organ support therapy (ventilatory support, vasoactive drugs, renal replacement therapy, artificial liver), or post-cardiopulmonary resuscitation.
View Article and Find Full Text PDF[Development and validation of a risk prediction model for early postoperative high intraocular pressure after implantation of posterior chamber intraocular lens].
Zhonghua Yi Xue Za Zhi
January 2025
Ophthalmology Center, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, China.
To develop and validate a predictive model for assessing the risk of early postoperative high intraocular pressure (HIOP) following posterior chamber intraocular lens implantation. The clinical data of patients who underwent posterior chamber intraocular lens implantation at the Second Affiliated Hospital of Zhejiang University School of Medicine between May 2023 and April 2024 were retrospectively reviewed. Patients were divided into a modeling group and a validation group with a 7∶3 ratio using computerized random allocation.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!