Failing to fail: clinicians’ experience of assessing underperforming dental students
Bibliographic record
Abstract
INTRODUCTION: Anecdotal evidence within a UK dental school indicated that staff's grading did not always match their evaluation of students' clinical proficiency. The invalid assessment of underperforming students, which has considerable ramifications, has been reported internationally for students of nursing and medicine, but a database search revealed no accounts for dental education. AIM: To develop an understanding of clinicians' approaches to assessing underperforming dental students. METHODOLOGY: Seventeen clinical staff were interviewed (eleven females, six males). Interviews were recorded and transcribed verbatim. A grounded theory methodology was used, with simultaneous data collection and analysis. The main analytical technique was constant comparison. FINDINGS: Participants' shared basic problem was Assessing undergraduate students, expressed as how they evaluated and used the assessment system or perceived others to do so. The core category, which explains what clinical staff do to manage their difficulties with assessment, was identified as Failing to Fail and has three subcategories: Evaluating the Assessment System, Shielding the Student and Protecting Myself. CONCLUSION: This study has substantiated the complexity of failing to fail and confirmed that some causes are shared across healthcare professions, although insufficient staff discussion, the avoidance of confrontation and the impact of negative student attitude are not reported elsewhere or are minor findings. It is recommended that clinical staff receive additional training in assessment and that they are made more aware of their learning needs, their attitudes and beliefs. Increased discussion between staff about assessment and about students known to be in difficulty is essential.
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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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".