An online programme in teaching and assessing critical thinking for medical faculty
Bibliographic record
Abstract
Introduction: For all clinical providers in healthcare, decision-making is a critical feature of everything they do. Every day physicians engage in clinical decision-making where knowledge, evidence, experience, and interpretation of clinical data are used to produce decisions, yet, it is fair to say that most do not have an explicit insight or understanding of this complex process. In particular, few will have training in teaching and assessing the cognitive and affective factors that underlie clinical decision-making. Methods: To foster an increased awareness and understanding of these factors, the Dalhousie Critical Thinking Program was established with the mandate to develop and deliver curriculum for critical thinking in the 4-year undergraduate program. To assist teaching faculty with the goal and objectives of the program, the Teaching and Assessing Critical Thinking Program (TACT) was introduced. Results: Using the dual process model as a platform for decision-making, this program introduces general principles of critical thinking and provides tools to teach learners how to strengthen their critical thinking skills. To offer flexible learning, an online approach was chosen for delivery of the program. Conclusion: To date, we have offered eleven iterations of Part 1 to a total of 261 participants and six iterations of Part 2 to a total of 89 participants. Evaluations show the online approach to content delivery was well received and the content to be of practical use.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".