Dataset Information

Applying inter-rater reliability to improve consistency in classifying PhD career outcomes.

ABSTRACT: Background: There has been a groundswell of national support for transparent tracking and dissemination of PhD career outcomes. In 2017, individuals from multiple institutions and professional organizations met to create the Unified Career Outcomes Taxonomy (UCOT 2017), a three-tiered taxonomy to help institutions uniformly classify career outcomes of PhD graduates. Early adopters of UCOT 2017, noted ambiguity in some categories of the career taxonomy, raising questions about its consistent application within and across institutions. Methods: To test and evaluate the consistency of UCOT 2017, we calculated inter-rater reliability across two rounds of iterative refinement of the career taxonomy, classifying over 800 PhD alumni records via nine coders. Results: We identified areas of discordance in the taxonomy, and progressively refined UCOT 2017 and an accompanying Guidance Document to improve inter-rater reliability across all three tiers of the career taxonomy. However, differing interpretations of the classifications, especially for faculty classifications in the third tier, resulted in continued discordance among the coders. We addressed this discordance with clarifying language in the Guidance Document, and proposed the addition of a flag system for identification of the title, rank, and prefix of faculty members. This labeling system provides the additional benefit of highlighting the granularity and the intersectionality of faculty job functions, while maintaining the ability to sort by - and report data on - faculty and postdoctoral trainee roles, as is required by some national and federal reporting guidelines. We provide specific crosswalk guidance for how a user may choose to incorporate our suggestions while maintaining the ability to report in accordance with UCOT 2017. Conclusions: Our findings underscore the importance of detailed guidance documents, coder training, and periodic collaborative review of career outcomes taxonomies as PhD careers evolve in the global workforce. Implications for coder-training and use of novice coders are also discussed.

SUBMITTER: Stayart CA

PROVIDER: S-EPMC7014580 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Applying inter-rater reliability to improve consistency in classifying PhD career outcomes.

Stayart C Abigail CA Brandt Patrick D PD Brown Abigail M AM Dahl Tamara T Layton Rebekah L RL Petrie Kimberly A KA Flores-Kim Emma N EN Peña Christopher G CG Fuhrmann Cynthia N CN Monsalve Gabriela C GC

F1000Research 20200109

<b>Background:</b> There has been a groundswell of national support for transparent tracking and dissemination of PhD career outcomes. In 2017, individuals from multiple institutions and professional organizations met to create the Unified Career Outcomes Taxonomy (UCOT 2017), a three-tiered taxonomy to help institutions uniformly classify career outcomes of PhD graduates. Early adopters of UCOT 2017, noted ambiguity in some categories of the career taxonomy, raising questions about its consiste ...[more]

PMID: 32089837

Similar Datasets

Project description:PurposeFirst, to evaluate inter-rater reliability when human raters estimate the reading performance of visually impaired individuals using the MNREAD acuity chart. Second, to evaluate the agreement between computer-based scoring algorithms and compare them with human rating.MethodsReading performance was measured for 101 individuals with low vision, using the Portuguese version of the MNREAD test. Seven raters estimated the maximum reading speed (MRS) and critical print size (CPS) of each individual MNREAD curve. MRS and CPS were also calculated automatically for each curve using two different algorithms: the original standard deviation method (SDev) and a non-linear mixed effects (NLME) modeling. Intra-class correlation coefficients (ICC) were used to estimate absolute agreement between raters and/or algorithms.ResultsAbsolute agreement between raters was 'excellent' for MRS (ICC = 0.97; 95%CI [0.96, 0.98]) and 'moderate' to 'good' for CPS (ICC = 0.77; 95%CI [0.69, 0.83]). For CPS, inter-rater reliability was poorer among less experienced raters (ICC = 0.70; 95%CI [0.57, 0.80]) when compared to experienced ones (ICC = 0.82; 95%CI [0.76, 0.88]). Absolute agreement between the two algorithms was 'excellent' for MRS (ICC = 0.96; 95%CI [0.91, 0.98]). For CPS, the best possible agreement was found for CPS defined as the print size sustaining 80% of MRS (ICC = 0.77; 95%CI [0.68, 0.84]). Absolute agreement between raters and automated methods was 'excellent' for MRS (ICC = 0.96; 95% CI [0.88, 0.98] for SDev; ICC = 0.97; 95% CI [0.95, 0.98] for NLME). For CPS, absolute agreement between raters and SDev ranged from 'poor' to 'good' (ICC = 0.66; 95% CI [0.3, 0.80]), while agreement between raters and NLME was 'good' (ICC = 0.83; 95% CI [0.76, 0.88]).ConclusionFor MRS, inter-rater reliability is excellent, even considering the possibility of noisy and/or incomplete data collected in low-vision individuals. For CPS, inter-rater reliability is lower. This may be problematic, for instance in the context of multisite investigations or follow-up examinations. The NLME method showed better agreement with the raters than the SDev method for both reading parameters. Setting up consensual guidelines to deal with ambiguous curves may help improve reliability. While the exact definition of CPS should be chosen on a case-by-case basis depending on the clinician or researcher's motivations, evidence suggests that estimating CPS as the smallest print size sustaining about 80% of MRS would increase inter-rater reliability.

Project description:BACKGROUND:There is a growing trend in the use of mobile health (mHealth) technologies in traditional Chinese medicine (TCM) and telemedicine, especially during the coronavirus disease (COVID-19) outbreak. Tongue diagnosis is an important component of TCM, but also plays a role in Western medicine, for example in dermatology. However, the procedure of obtaining tongue images has not been standardized and the reliability of tongue diagnosis by smartphone tongue images has yet to be evaluated. OBJECTIVE:The first objective of this study was to develop an operating classification scheme for tongue coating diagnosis. The second and main objective of this study was to determine the intra-rater and inter-rater reliability of tongue coating diagnosis using the operating classification scheme. METHODS:An operating classification scheme for tongue coating was developed using a stepwise approach and a quasi-Delphi method. First, tongue images (n=2023) were analyzed by 2 groups of assessors to develop the operating classification scheme for tongue coating diagnosis. Based on clinicians' (n=17) own interpretations as well as their use of the operating classification scheme, the results of tongue diagnosis on a representative tongue image set (n=24) were compared. After gathering consensus for the operating classification scheme, the clinicians were instructed to use the scheme to assess tongue features of their patients under direct visual inspection. At the same time, the clinicians took tongue images of the patients with smartphones and assessed tongue features observed in the smartphone image using the same classification scheme. The intra-rater agreements of these two assessments were calculated to determine which features of tongue coating were better retained by the image. Using the finalized operating classification scheme, clinicians in the study group assessed representative tongue images (n=24) that they had taken, and the intra-rater and inter-rater reliability of their assessments was evaluated. RESULTS:Intra-rater agreement between direct subject inspection and tongue image inspection was good to very good (Cohen ? range 0.69-1.0). Additionally, when comparing the assessment of tongue images on different days, intra-rater reliability was good to very good (? range 0.7-1.0), except for the color of the tongue body (?=0.22) and slippery tongue fur (?=0.1). Inter-rater reliability was moderate for tongue coating (Gwet AC2 range 0.49-0.55), and fair for color and other features of the tongue body (Gwet AC2=0.34). CONCLUSIONS:Taken together, our study has shown that tongue images collected via smartphone contain some reliable features, including tongue coating, that can be used in mHealth analysis. Our findings thus support the use of smartphones in telemedicine for detecting changes in tongue coating.

Dataset Information

Applying inter-rater reliability to improve consistency in classifying PhD career outcomes.

Publications

Applying inter-rater reliability to improve consistency in classifying PhD career outcomes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets