Applying Prochaska’s model of change to needs assessment, programme planning and outcome measurement
Bibliographic record
Abstract
A major goal of continuing medical education (CME) is to enhance the performance of the learner. In order to accomplish this goal, careful consideration and expertise must be applied to the three primary ingredients of CME planning: assessing learner needs, programme design and outcome measurement. Traditional methods used to address these three components seldom result in CME initiatives that change performance, even in the presence of sophisticated CME formats and capable learners. In part, performance may not change because the learner is not 'ready to change'. Planners of CME are aware of this concept but have been unable to measure 'readiness to change' or employ it in assessing learner needs, and planning and evaluating CME. One theory that focuses on an individual's readiness to change is Prochaska's model, which postulates that change is a gradual process proceeding through specific stages, each of which has key characteristics. This paper examines the applicability of this model to all components of CME planning. To illustrate the importance of this model, this paper provides examples of these three components conducted both with and without implementation of this model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".