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Record W4322620256 · doi:10.2147/amep.s402059

Utilizing Evaluation and Development Frameworks to Engineer a College-Wide Evaluation and Reform of an Undergraduate Dental Curriculum

2023· article· en· W4322620256 on OpenAlexfundno aff
Abdurahman Alwadei

Bibliographic record

VenueAdvances in Medical Education and Practice · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
FundersSchool of Dentistry, University of MarylandUniversity of Texas Health Science Center at HoustonMcGill UniversityUniversity of TorontoUniversity of Louisville
KeywordsOperationalizationCurriculumOptimal distinctiveness theoryHindsight biasMedical educationProcess (computing)Data collectionFocus groupDental educationComputer scienceEngineering ethicsPsychologyMedicinePedagogyEngineeringSociology

Abstract

fetched live from OpenAlex

Purpose: To operationalize and analyze a college-wide evaluation of an undergraduate dental curriculum. Materials and Methods: A descriptive case study design was used with extensive multiple data collection methods that included literature review, document review of existing data, survey questionnaires, focus group semi-structured interviews and observation of clinical and laboratory tasks. This approach was based on Kern's curriculum development model and Fitzpatrick's practical guidelines and evaluation standards. Results: The evaluation outcomes indicated that a significant curricular change is needed. In hindsight, a thorough reflection on the evaluation strategy is provided highlighting several contextual factors. Actionable recommendations and comparisons are also drafted to shape a coherent curriculum reform implementation. Conclusion: The process by which the evaluation was conducted, and the reform implementation is being instituted, while unique to this college, may offer insights for change at other dental colleges. In that, greater emphasis is placed on the general principles that remain applicable to other comparable contexts regardless of the distinctiveness in specificities.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.447
Teacher spread0.422 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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