Assessing the Clinical Skills of Dental Students: A Review of the Literature
Bibliographic record
Abstract
Education, from a student perspective, is largely driven by assessment. An effective assessment tool should beboth valid and reliable, yet this is often not achieved. The aim of this literature review is to identify and appraisethe evidence base for assessment tools used primarily in evaluating clinical skills of dental students.Methods: MEDLINE was searched for all relevant articles from January 1950- January 2011 published in theEnglish language. References of the articles were then hand searched.This review begins with a brief outline of the student learning process and the aim of assessment. The toolsavailable for both formative and summative assessments are discussed, with particular reference to those used inassessing dental students’ clinical ability. The problems of subjectivity and assessor variability associated withtraditional teacher-led assessments are highlighted. Methods which have attempted to overcome these problems,such as the use of checklists and training are then discussed. The benefits and shortcomings of the use ofstudents as assessors, both in self and peer assessment are reviewed. Finally, the use of objective assessmentmethods involving opto-electronic and haptic technology is considered.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".