Lessons from the implementation of developmental progress assessment: A scoping review
Bibliographic record
Abstract
OBJECTIVES: Educators and researchers recently implemented developmental progress assessment (DPA) in the context of competency-based education. To reap its anticipated benefits, much still remains to be understood about its implementation. In this study, we aimed to determine the nature and extent of the current evidence on DPA, in an effort to broaden our understanding of the major goals and intended outcomes of DPA as well as the lessons learned from how it has been executed in, or applied across, educational contexts. METHODS: We conducted a scoping study based on the methodology of Arksey and O'Malley. Our search strategy yielded 2494 articles. These articles were screened for inclusion and exclusion (90% agreement), and numerical and qualitative data were extracted from 56 articles based on a pre-defined set of charting categories. The thematic analysis of the qualitative data was completed with iterative consultations and discussions until consensus was achieved for the interpretation of the results. RESULTS: Tools used to document DPA include scales, milestones and portfolios. Performances were observed in clinical or standardised contexts. We identified seven major themes in our qualitative thematic analysis: (a) underlying aims of DPA; (b) sources of information; (c) barriers; (d) contextual factors that can act as barriers or facilitators to the implementation of DPA; (e) facilitators; (f) observed outcomes, and (g) documented validity evidences. CONCLUSIONS: Developmental progress assessment seems to fill a need in the training of future competent health professionals. However, moving forward with a widespread implementation of DPA, factors such as lack of access to user-friendly technology and time to observe performance may render its operationalisation burdensome in the context of competency-based medical education.
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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 | 0.254 | 0.488 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".