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Uncovering the Perceptions of Grades in Dentistry Students – How Grades are Impacting Learning & Clinical Development

2022· article· en· W4225373593 on OpenAlexaff
Jenna J. Yuen, Andrew J. Horne, Tyler S. Beveridge, Sarah McLean

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsWestern University
Fundersnot available
KeywordsThematic analysisFocus groupMedical educationPerceptionComprehensionPsychologyQualitative researchPopulationEmpathyHealth careMedicineQualitative propertySocial psychology

Abstract

fetched live from OpenAlex

Introduction Grades in healthcare professional schools may be doing more harm than good. In fact, educators and researchers suggest grades have lasting negative impacts on professional development and the ability to navigate clinical settings (i.e., impairs self‐regulated learning, interferes with developing patient‐care competencies, and decreases levels of empathy). Although some studies have incorporated grade perceptions into their investigations, we lack research dedicated to primarily understanding the student’s perception of grades, and whether they think grades help or hinder the quality of their clinical learning. Aims The first aim of this study is to identify the most prevalent grade perceptions among dentistry students. The second aim is to explore the underlying contributing factors and to determine how dentistry students’ grade perceptions are influencing their learning and clinical development. Methods To address the first aim, we distributed an online survey to dentistry students at the Schulich School of Medicine & Dentistry. To address our second aim, we conducted virtual semi‐structured interviews and a focus group on the same population of dentistry students. Survey results guided and informed interview/focus group discussions. Qualitative data was analyzed through an inductive thematic process. Results Fourteen grade perception themes were identified. The three most frequent perceptions were grades as a tool for ranking students, an evaluation tool to test knowledge and comprehension, and a feedback tool on comprehension. Seventy students participated in the survey; 70% agreed that grades guided their learning/studying strategies; 91% agreed grades reflected their ability to memorize content; 85% did not think grades accurately represented their practical skills; and 89% agreed that grades determined their acceptance into specialty/residency programs. Nine students participated in the interviews/focus group. Emerging topics included the importance of grades during specialty/residency applications, the disconnect between grades on a 0‐100% scale and clinical development, and mixed opinions about grades facilitating learning. Characterization of dentistry students’ perceptions towards grades in a clinical learning environment is ongoing. Conclusion Dentistry students commonly perceive grades as a ranking tool because of its central role in specialty/residency applications. While some note that grades help their learning, the majority of students agree that grades reflect memorization of content. Our data also suggest students do not find grades on the 0‐100% scale helpful in their clinical development because it does not indicate whether professionally acceptable standards are met. Findings obtained from our study brings students’ voices to the forefront of the ongoing discussion about grades, and may provide important considerations for future healthcare professional curricula.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.066
GPT teacher head0.409
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainEvaluation
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2022
Admission routes1
Has abstractyes

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