A Meta-Analysis of the Effect of Paper Versus Digital Reading on Reading Comprehension in Health Professional Education
Bibliographic record
Abstract
Objective. Despite a rise in the use of digital education in health professional education (HPE), little is known about the comparative effectiveness of paper-based reading and its digital alternative on reading comprehension. The objectives of this study were to identify, appraise, and synthesize the evidence regarding the effect of how media is read on reading comprehension in the context of HPE. Methods. Observational, quasi-experimental, and experimental studies published before April 16, 2021, were included if they compared the effectiveness of paper-based vs digital-based reading on reading comprehension among HPE students, trainees, and residents. Random-effects meta-analyses were performed using standardized mean differences. Results. From a pool of 2,208 references, we identified and included 10 controlled studies that had collectively enrolled 817 participants. Meta-analyses revealed a slight but nonsignificant advantage to students reading paper-based HPE texts rather than digital text (standardized mean difference, -0.08; 95% CI -0.28 to 0.12). Subgroup analyses revealed that students reading HPE-related texts had better reading comprehension when reading text on paper rather than digitally (SMD = -0.36; 95% CI -0.69 to -0.03). Heterogeneity was low in all analyses. The quality of evidence was low because of risks of bias across studies. Summary. Current evidence suggests little to no difference in students’ comprehension when reading HPE texts on paper vs digitally. However, we observed effects favoring reading paper-based texts when texts relevant to the students’ professional discipline were considered. Rigorous studies are needed to confirm this finding and to evaluate new means of boosting reading comprehension among students in HPE programs.
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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 | 0.019 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.044 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".