Navigating Discordance: Assessing Varied Applications of the American Academy of Periodontology In-Service Examination in Postdoctoral Programs
Bibliographic record
Abstract
Introduction The American Academy of Periodontology (AAP) In-Service Examination (ISE) is offered annually to residents in postdoctoral periodontal programs across the United States and Canada. The language in AAP published guidance supports both formative and summative uses of the exam, presenting discordance in how to use and interpret the AAPISE. It is important to clarify how programs use data, as formative assessments are low-stakes and aim to provide feedback to learners for improving their learning. On the other hand, summative assessments are used to make high-stakes decisions, such as program admission, graduation, and licensure. These two types of assessments serve different purposes and have distinct outcomes. This study aimed to explore and characterize how stakeholders utilize the AAPISE and its associated score reports. Methods Semi-structured interviews of periodontology program directors, department chairs, and residents were conducted in 2022. Data from these interviews were explored, coded, and thematically analyzed. Results A total of 16 interviews were conducted and analyzed: four chairs, eight program directors, and four residents representing the experience at 20 postdoctoral periodontal programs. Five major themes were identified regarding the use of the AAPISE: formative assessment, board preparation, program development, promoting a culture of achievement, and summative assessment. Conclusion This study's findings suggest varied uses of the AAPISE amongst postdoctoral periodontal program stakeholders, reflecting the variance within the AAP guidance. The discordant uses and guidance jeopardize the AAPISE’s potential to align with the 2018 Consensus Framework for Good Assessment elements. The authors propose recommendations to the AAP and stakeholders for future use.
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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 | 0.046 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".