Utilizing Evaluation and Development Frameworks to Engineer a College-Wide Evaluation and Reform of an Undergraduate Dental Curriculum
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.309 | 0.227 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".