Evaluation of the Effects of an Evidence-Based Practice Curriculum on Knowledge, Attitudes, and Self-Assessed Skills and Behaviors in Chiropractic Students
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate the effects of an evidence-based practice (EBP) curriculum incorporated throughout a chiropractic doctoral program on EBP knowledge, attitudes, and self-assessed skills and behaviors in chiropractic students. METHODS: In a prospective cohort design, students from the last entering class under an old curriculum were compared with students in the first 2 entering classes under a new EBP curriculum during the 9th and 11th quarters of the 12-quarter doctoral program at the University of Western States in Portland, OR (n = 370 students at matriculation). Analysis of variance (ANOVA) was performed using a 3-cohort × 2-quarter repeated cross-sectional factorial design to assess the effect of successive entering classes and stage of the students' education. RESULTS: For the knowledge exam (primary outcome), there was a statistically significant cohort effect with each succeeding cohort showing better performance (P < .001); students also performed slightly better in the 11th quarter than in the 9th quarter (P < .05). A similar pattern in cohort and quarter effects was found with behavior self-appraisal for greater time accessing databases such as PubMed. Student self-appraisal of their skills was higher in the 11th than the 9th quarter. All cohorts rejected a set of sentinel misconceptions about application of scientific literature (practice attitudes). CONCLUSIONS: The implementation of the EBP curriculum at this institution resulted in acquisition of knowledge necessary to access and interpret scientific literature, the retention and improvement of skills over time, and the enhancement of self-reported behaviors favoring use of quality online resources.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 3 models reading the full record.
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".