De-fuzzification of reflection in the education of health professionals
Bibliographic record
Abstract
Our educational institutions are mandated to equip future physicians and other health care professionals with the scientific, craft, and inter-personal knowledge and skills to meet the demands of contemporary clinical practice. Clinicians must acquire advanced communication skills, develop the ability to manage complex situations, make appropriate use of medical knowledge and technology, and problem-solve through the exercise of refined judgment. The ability to reflect in and on situations of this nature is considered a necessary professional aptitude in order to ensure effective and compassionate whole person care. Notwithstanding the general acceptance of these premises, ‘reflection’ remains a fuzzy concept. It is a polysemous term that has proved difficult to define and has attracted to itself numerous false claims and unfulfilled promises. Excellence in reflective abilities is notoriously difficult to recognize in another individual and it may not be ‘teachable’. Furthermore, there have been recurring doubts as to the feasibility of meaningfully assessing reflection.We intend to explore these issues in this session. We will demonstrate how reflection can be role-modeled and inculcated. Instructional Methods This will be an interactive workshop. Learning Objectives By the end of the workshop, participants will be able to:• clarify the concept of reflection and understand its application to the education of health professionals• discuss a framework, including specific methods, for the structuring and deployment of an educational program aimed at promoting reflection.
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 | 0.042 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".