Assessment Practices in Continuing Professional Development Activities in Health Professions: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: In continuing professional development (CPD), educators face the need to develop and implement innovative assessment strategies to adhere to accreditation standards and support lifelong learning. However, little is known about the development and validation of these assessment practices. We aimed to document the breadth and depth of what is known about the development and implementation of assessment practices within CPD activities. METHODS: We conducted a scoping review using the framework proposed by Arksey and O'Malley (2005) and updated in 2020. We examined five databases and identified 1733 abstracts. Two team members screened titles and abstracts for inclusion/exclusion. After data extraction, we conducted a descriptive analysis of quantitative data and a thematic analysis of qualitative data. RESULTS: A total of 130 studies were retained for the full review. Most reported assessments are written assessments (n = 100), such as multiple-choice items (n = 79). In 99 studies, authors developed an assessment for research purpose rather than for the CPD activity itself. The assessment validation process was detailed in 105 articles. In most cases, the authors examined the content with experts (n = 57) or pilot-tested the assessment (n = 50). We identified three themes: 1-satisfaction with assessment choices; 2-difficulties experienced during the administration of the assessment; and 3-complexity of the validation process. DISCUSSION: Building on the adage "assessment drives learning," it is imperative that the CPD practices contribute to the intended learning and limit the unintended negative consequences of assessment. Our results suggest that validation processes must be considered and adapted within CPD contexts.
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.
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 | 0.079 | 0.248 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.030 | 0.034 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".