The use of portfolios for assessment of the competence and performance of doctors in practice
Bibliographic record
Abstract
BACKGROUND: The use of portfolios can potentially provide flexibility in the summative assessment of doctors in practice. An assessment system should reflect and reinforce the active and planned professional development goals of individual doctors. This paper discusses some of the issues involved in developing such a system. RESULTS: To provide a complete picture of an individual doctor's practice, we suggest that a portfolio should encompass: (1) evidence covering all three domains of patient care, personal development and context management; (2) evidence that the person continuously undertakes critical assessment of their own performance, identifies and prioritises areas requiring enhanced performance and takes action to improve them as appropriate; (3) evidence that has been generated by assessments that are acceptably reliable, and (4) evidence which, taken in its entirety, is sufficient, valid, current and authentic. We include a suggested outline of the components of such a portfolio and suggest some criteria to determine the effectiveness of learning cycles. Portfolio reliability and validity requires sufficient evidence on which to base a judgement combined with reliable processes. CONCLUSION: Carefully specified portfolios can contribute to a system that ensures all doctors take an active part in identifying and meeting their own learning needs. Such a system, if properly implemented, would have a greatly beneficial impact on continuous quality improvement for the profession in general.
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 | 0.096 | 0.320 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".