Concept mapping toward competency: Teaching and assessing undergraduate evidence‐informed practice
Bibliographic record
Abstract
Abstract Background Undergraduate students in healthcare professions need to develop critical thinking skills in order to be prepared to deliver patient‐centered high‐quality care upon graduation. The new Canadian Athletic Therapy Association (CATA) competency framework includes a “scholar” role, placing emphasis on knowledge and skills related to evidence‐informed practices (EIP). Educators are expected to develop their student's EIP skills; however, little is known about the optimal educational approaches to accomplish this task. The study objective was to examine concept mapping as a teaching and learning strategy to deepen the understanding of EIP and examine the validity and reliability of a concept mapping scoring rubric. Method A concept mapping approach to teaching EIP was piloted in an upper‐level research methods and statistics course. Students (N = 15) participated in a pretest post‐test concurrent nested mixed‐method intervention study that analyzed the impact of concept mapping on students' understanding of EIP and assessed initial validity and reliability of the grading rubric. Results Students demonstrated a deeper understanding of EIP and its relationship to healthcare practice following the concept mapping activity compared to a note‐taking activity (Cohen's d = 1.79). The concept mapping rubric used to assess EIP competence demonstrated strong construct validity and content validity with moderate inter‐rater reliability. Conclusions Employing the concept mapping technique as a teaching and learning tool proved to be effective to teach EIP principles and concepts based on student grades. An analytic rubric method was found valid and reliable for grading student concept maps underpinning EIP competency.
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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 | 0.028 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".