121 Systematic review of learning theories in patient education
Bibliographic record
Abstract
Introduction Patient education is essential to health care, yet its development often lacks meaningful connection to and incorporation of established learning theories. This study aimed to systematically review evidence surrounding use of learning theories in patient education strategies. Methods A systematic search including terms like ‘learning theory’ and ‘patient education’ was done across five databases including MEDLINE and EBSCO. The main outcomes were knowledge retention or application of knowledge (via clinical outcomes). Results From 4,449 articles, 347 underwent full-text review and 46 were included in data extraction (figure 1). Predominant learning theories reported were Social Learning/Cognitive Theory (72%), and adult learning theory (13%). Most common interventions were group sessions (50%) with discussions (33%). Studies assessed knowledge retention (72%) and application (63%). 83% of studies had a control group and of those, 66% of interventions were better than control. Included studies often did not report the level of detail needed for additional evaluation and had different levels of learning theory integration. On a 5-point scale, only 52% of studies were clear or very clear on their incorporation of learning theories in the intervention. Discussion Significant heterogeneity of the methods and results made it difficult to assess impact of theories/strategies on outcomes. Few education interventions are theory-based, and the extent of theory integration into current studies varied widely. More guidance on the incorporation of learning theories in patient education is essential. Conclusion(s) This study highlights the need for improved guidance in creating quality learning-theory based patient education materials.
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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 | 0.036 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".