Dataset Information

The Confidence Database.

ABSTRACT: Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4?million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects.

SUBMITTER: Rahnev D

PROVIDER: S-EPMC7565481 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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The Confidence Database.

Rahnev Dobromir D Desender Kobe K Lee Alan L F ALF Adler William T WT Aguilar-Lleyda David D Akdoğan Başak B Arbuzova Polina P Atlas Lauren Y LY Balcı Fuat F Bang Ji Won JW Bègue Indrit I Birney Damian P DP Brady Timothy F TF Calder-Travis Joshua J Chetverikov Andrey A Clark Torin K TK Davranche Karen K Denison Rachel N RN Dildine Troy C TC Double Kit S KS Duyan Yalçın A YA Faivre Nathan N Fallow Kaitlyn K Filevich Elisa E Gajdos Thibault T Gallagher Regan M RM de Gardelle Vincent V Gherman Sabina S Haddara Nadia N Hainguerlot Marine M Hsu Tzu-Yu TY Hu Xiao X Iturrate Iñaki I Jaquiery Matt M Kantner Justin J Koculak Marcin M Konishi Mahiko M Koß Christina C Kvam Peter D PD Kwok Sze Chai SC Lebreton Maël M Lempert Karolina M KM Ming Lo Chien C Luo Liang L Maniscalco Brian B Martin Antonio A Massoni Sébastien S Matthews Julian J Mazancieux Audrey A Merfeld Daniel M DM O'Hora Denis D Palser Eleanor R ER Paulewicz Borysław B Pereira Michael M Peters Caroline C Philiastides Marios G MG Pfuhl Gerit G Prieto Fernanda F Rausch Manuel M Recht Samuel S Reyes Gabriel G Reyes Gabriel G Rouault Marion M Sackur Jérôme J Sadeghi Saeedeh S Samaha Jason J Seow Tricia X F TXF Shekhar Medha M Sherman Maxine T MT Siedlecka Marta M Skóra Zuzanna Z Song Chen C Soto David D Sun Sai S van Boxtel Jeroen J A JJA Wang Shuo S Weidemann Christoph T CT Weindel Gabriel G Wierzchoń Michał M Xu Xinming X Ye Qun Q Yeon Jiwon J Zou Futing F Zylberberg Ariel A

Nature human behaviour 20200203 3

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset ...[more]

PMID: 32015487

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Project description:BackgroundDespite similar performance metrics, women medical trainees routinely self-assess their own skills lower than men. The phenomenon of a "confidence gap" between genders, where women report lower self-confidence independent of actual ability or competency, may have an important interaction with gender differences in assessment. Identifying whether there are gender-based differences in how confidence is mentioned in written evaluations is a necessary step to understand the interaction between evaluation and the gender-based confidence gap.ObjectiveTo analyze faculty evaluations of internal medicine (IM) residents for gender-based patterns in the use of iterations of "confidence."DesignWe performed a retrospective cohort study of all inpatient faculty evaluations of University of Pennsylvania IM residents from 2018 to 2021. We performed n-gram text-mining to identify evaluations containing the terms "confident," "confidence," or "confidently." We performed univariable and multivariable logistic regression to determine the association between resident gender and references to confidence (including comments reflecting too little confidence), adjusting for faculty gender, post-graduate year (PGY), numeric rating, and service.SubjectsUniversity of Pennsylvania IM residents from 2018 to 2021.Key resultsThere were 5416 evaluations of IM residents (165 women [51%], 156 men [49%]) submitted by 356 faculty members (149 women [51%]), of which 7.1 % (n=356) contained references to confidence. There was a significant positive association between the mention of confidence and women resident gender (OR 1.54, CI 1.23-1.92; p<0.001), which persisted after adjustment for faculty gender, numeric rating, and PGY level. Eighty evaluations of the cohort explicitly mentioned the resident having "too little confidence," which was also associated with women resident gender (OR 1.66, CI 1.05-2.62; p=0.031).ConclusionNarrative evaluations of women residents were more likely to contain references to confidence, after adjustment for numerical score, PGY level, and faculty gender, which may perpetuate the gender-based confidence gap, introduce bias, and ultimately impact professional identity development.

Dataset Information

The Confidence Database.

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The Confidence Database.

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