Assessing students’ confidence in interpreting dental radiographs following a blended learning module
Bibliographic record
Abstract
OBJECTIVE: This study assessed senior dental hygiene (DH) students' self-reported confidence in interpreting dental radiographs following the introduction of a blended learning (BL) module for radiology interpretation. The assessment of students was conducted five months prior to graduation. METHODS: A BL oral radiology module was designed. In order to capture the context, descriptions and differences of students' experience and confidence, a qualitative research approach was selected. Data were captured using a semi-structured interview process and analysed using phenomenographic methods. RESULTS: Sixteen students were interviewed. Blinded transcripts were analysed, and the main themes relating to confidence were extracted and arranged into categories. The categories were coded as to how confident (low, medium or high) each of the students felt specific to varying contexts and complexities of radiographic interpretation. CONCLUSION: Predominately, the BL model had a positive impact on DH students' confidence in the interpretation of radiographic findings. However, when asked about their level of overall confidence in interpreting dental radiographs, students still did not describe themselves as confident for all potential findings on radiographs at this point in their education. The students highlighted the importance of having patient history details and clinical assessment findings included in the interpretation exercises and expressed a desire to collaborate with other professionals when interpreting radiographs.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".