Effects of reading media on reading comprehension in health professional education: a systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of digital-based reading versus paper-based reading on reading comprehension among students, trainees, and residents participating in health professional education. INTRODUCTION: Several reviews have examined the effects of reading media on reading comprehension; however, none have considered health professional education specifically. The growing use of electronic media in health professional education, as well as recent data on the consequences of digital-based reading on learning, justify the necessity to review the current literature to provide research and educational recommendations. INCLUSION CRITERIA: Studies conducted with health professions students, trainees, and residents individually receiving educational material written in their first language in a paper-based or a digital-based format will be considered. Studies conducted among participants with cognitive impairment or reading difficulties will be excluded. Observational, experimental and quasi-experimental studies that assess reading comprehension measured by previously validated or researcher-generated tests will be considered. METHODS: Relevant studies will be sought from CINAHL, Embase, ERIC, Google Scholar, MEDLINE, PsycINFO, and Web of Science (SCI and SSCI), without date or language restrictions. Two independent reviewers will perform title and abstract screening, full-text review, critical appraisal, and data extraction. Disagreements will be resolved through discussion or with a third independent reviewer. Synthesis will occur at four levels (i.e., study, participant, intervention, and outcome levels) in a table format. Data will be synthesized descriptively and with meta-analyses if appropriate. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42020154519.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.077 | 0.085 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.057 | 0.008 |
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".