A qualitative study of trainer and trainee perceptions and experiences of clinical assessment in post‐graduate dental training
Bibliographic record
Abstract
BACKGROUND: The implementation of workplace-based assessment (WBA) needs to ensure the achievement of pre-set competences but may look different across varying contexts, such as in post-graduate dental education. The purpose of this study is to explore the perception of residents, faculty members and alumni concerning their experience with clinical assessment, and what configurations they consider as optimal to maximise the entailed learning experience. METHODS: This study relied on a qualitative descriptive design using two data collection tools: focus group sessions, and semi-structured, one-to-one interviews. Data were triangulated from three sources: residents, faculty members and alumni. The data were inductively analysed based on constructivist epistemology. This was done using the Thematic Analysis approach, facilitated by NVivo software. RESULTS: The analysis revealed two mutually exclusive themes: process and people. Within process, variables related to quality, workflow and feedback surfaced. As for the people theme, the main two group of stakeholders referred to in the related analysis were the trainees and the trainers. DISCUSSION: There are many variables that need to be considered when developing an evidence-driven WBA. In addition, factoring into the design of the WBA the perception of the main stakeholders will enable contextualisation which is expected to raise the reliability of the adapted tools. CONCLUSION: This study introduced a framework that could support post-graduate universities in their journey towards developing context-specific WBA.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".