The application of the challenge point framework in medical education
Bibliographic record
Abstract
OBJECTIVES: The current paper describes a model of learning that has been used to produce efficient learning, thus yielding greater retention of information and superior performance under stress. In this paper, the model is applied to the learning of technical skills. STRUCTURE: After a brief review of the learning-performance paradox and other relevant literature from the field of movement science, the benefits of challenge and adversity for learning are discussed in the context of a framework for learning known as the challenge point framework (CPF). The framework is based on laboratory and field studies of methods that have been shown to consistently enhance learning, and is used to model and generate insight into the relationships between practice protocols and the learning that results from them. APPLICATION: The practical application of the CPF to simulation-based medical education and training is described. Firstly, a simple conceptual model that utilises three key elements to adjust the functional difficulty of the tasks to be learned is outlined. Secondly, a number of assessment strategies that may be necessary to ensure that the trainee remains in the optimal learning zone are proposed. Thirdly, a practical example is used to demonstrate how to utilise this conceptual model to design simulation environments suitable for teaching an endotracheal intubation task to beginners and more advanced trainees.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".