Failing to fail: clinicians’ experience of assessing underperforming dental students
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it