Evaluating a dental sleep apnea mini‐residency program using the Kirkpatrick model
Bibliographic record
Abstract
OBJECTIVE: The aim of this retrospective study was to evaluate a Dental Sleep Medicine Mini-Residency (DSMMR) continuing education (CE) program using the Kirkpatrick model. METHODS: After receiving ethical approval, data from participants in the 2019-2020 DSMMR CE course were included for the Kirkpatrick evaluation. The analysis was stratified and all the Kirkpatrick levels were integrated: level 1 (satisfaction) was assessed via Likert scale and open-ended questions; level 2 (learning) was evaluated using pretest and posttest knowledge data following Module 1 (M1) and an assessment of multiple-choice questions (MCQs) developed by participants; level 3 (behavior) was evaluated using Likert scale questions; and level 4 (results) was assessed via the percentage of participants who passed the American Board of Dental Sleep Medicine (ABDSM) examination on their first attempt. RESULTS: A total of 90 participants were included in the study. At least 83.1% of participants agreed/strongly agreed with positively worded statements about satisfaction. Knowledge scores significantly increased from pre-M1 to post-M1 (p < 0.001); however, only 15.2% of MCQs were evaluated as well-formulated. At least 88.6% of participants agreed/strongly agreed with positively worded statements about transfer of knowledge/skills to their practice. 91.1% passed the ABDSM examination on their first attempt. CONCLUSION: The evaluation of the 2019-2020 DSMMR using the Kirkpatrick model suggests its overall positive impact as a training program. The Kirkpatrick model provided information that can be used to improve the quality of a program. Future studies should assess other dental CE programs using the Kirkpatrick model or another evaluation model.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".