Linking assessment to learning: a new route to quality assurance in medical practice
Bibliographic record
Abstract
BACKGROUND: If continuing professional development is to work and be sensible, an understanding of clinical practice is needed, based on the daily experiences of doctors within the multiple factors that determine the nature and quality of practice. Moreover, there must be a way to link performance and assessment to ensure that ongoing learning and continuing competence are, in reality, connected. Current understanding of learning no longer holds that a doctor enters practice thoroughly trained with a lifetime's storehouse of knowledge. Rather a doctor's ongoing learning is a 'journey' across a practice lifetime, which involves the doctor as a person, interacting with their patients, other health professionals and the larger societal and community issues. OBJECTIVES: In this paper, we describe a model of learning and practice that proposes how change occurs, and how assessment links practice performance and learning. We describe how doctors define desired performance, compare actual with desired performance, define educational need and initiate educational action. METHOD: To illustrate the model, we describe how doctor performance varies over time for any one condition, and across conditions. We discuss how doctors perceive and respond to these variations in their performance. The model is also used to illustrate different formative and summative approaches to assessment, and to highlight the aspects of performance these can assess. CONCLUSIONS: We conclude by exploring the implications of this model for integrated medical services, highlighting the actions and directions that would be required of doctors, medical and professional organisations, universities and other continuing education providers, credentialling bodies and governments.
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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 | 0.146 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.007 | 0.088 |
| Scholarly communication | 0.036 | 0.054 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".