Internet-Based Learning in the Health Professions
Bibliographic record
Abstract
CONTEXT: The increasing use of Internet-based learning in health professions education may be informed by a timely, comprehensive synthesis of evidence of effectiveness. OBJECTIVES: To summarize the effect of Internet-based instruction for health professions learners compared with no intervention and with non-Internet interventions. DATA SOURCES: Systematic search of MEDLINE, Scopus, CINAHL, EMBASE, ERIC, TimeLit, Web of Science, Dissertation Abstracts, and the University of Toronto Research and Development Resource Base from 1990 through 2007. STUDY SELECTION: Studies in any language quantifying the association of Internet-based instruction and educational outcomes for practicing and student physicians, nurses, pharmacists, dentists, and other health care professionals compared with a no-intervention or non-Internet control group or a preintervention assessment. DATA EXTRACTION: Two reviewers independently evaluated study quality and abstracted information including characteristics of learners, learning setting, and intervention (including level of interactivity, practice exercises, online discussion, and duration). DATA SYNTHESIS: There were 201 eligible studies. Heterogeneity in results across studies was large (I(2) > or = 79%) in all analyses. Effect sizes were pooled using a random effects model. The pooled effect size in comparison to no intervention favored Internet-based interventions and was 1.00 (95% confidence interval [CI], 0.90-1.10; P < .001; n = 126 studies) for knowledge outcomes, 0.85 (95% CI, 0.49-1.20; P < .001; n = 16) for skills, and 0.82 (95% CI, 0.63-1.02; P < .001; n = 32) for learner behaviors and patient effects. Compared with non-Internet formats, the pooled effect sizes (positive numbers favoring Internet) were 0.10 (95% CI, -0.12 to 0.32; P = .37; n = 43) for satisfaction, 0.12 (95% CI, 0.003 to 0.24; P = .045; n = 63) for knowledge, 0.09 (95% CI, -0.26 to 0.44; P = .61; n = 12) for skills, and 0.51 (95% CI, -0.24 to 1.25; P = .18; n = 6) for behaviors or patient effects. No important treatment-subgroup interactions were identified. CONCLUSIONS: Internet-based learning is associated with large positive effects compared with no intervention. In contrast, effects compared with non-Internet instructional methods are heterogeneous and generally small, suggesting effectiveness similar to traditional methods. Future research should directly compare different Internet-based interventions.
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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 | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".