Integrating evidence-based practice and information literacy skills in teaching physical and occupational therapy students
Bibliographic record
Abstract
BACKGROUND: To ensure that physical and occupational therapy graduates develop evidence-based practice (EBP) competencies, their academic training must promote EBP skills, such as posing a clinical question and retrieving relevant literature, and the information literacy skills needed to practice these EBP skills. OBJECTIVE: This article describes the collaborative process and outcome of integrating EBP and information literacy early in a professional physical therapy and occupational therapy programme. METHODS: The liaison librarian and a faculty member designed an instructional activity that included a lecture, workshop and assignment that integrated EBP skills and information literacy skills in the first year of the programme. The assignment was designed to assess students' ability to conduct a search independently. RESULTS: The lecture and workshop were successful in their objectives, as 101 of the 104 students received at least 8 out of 10 points on the search assignment. CONCLUSIONS: The teaching activities developed for the students in this course appear to have achieved the goal of teaching students the EBP research cycle so that they might begin to emulate it. The collaboration between the faculty member and the librarian was integral to the success of this endeavour. Future work will include the evaluation of students' long-term retention of information literacy objectives.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".