Medical students and residents appreciate ebooks’ convenience, but prefer the print book reading experience
Bibliographic record
Abstract
BACKGROUND: Increasingly, libraries buy medical monographs as ebooks, but that may not be what medical students and residents want. Some studies have shown that they prefer print books for some types of reading. On the other hand, for participants in distributed medical programs, ebooks are more accessible. OBJECTIVES: To determine whether medical students and residents at an institution with a distributed medical education program prefer medical ebooks or print books. METHODS: In February 2019, 844 medical students and residents were invited to complete an online questionnaire on their format preferences. RESULTS: Two hundred thirty-two students and residents responded. Most preferred electronic format for reading a few pages, but print for entire books. Respondents preferred ebooks because they were immediately available, searchable and could be used on the go, and print books because they strained users' eyes less, facilitated absorption of the text and could be held in users' hands. The location of respondents and year of study had little effect on responses. DISCUSSION: Libraries should consider buying quick reference and large, heavy textbooks as ebooks and pocket-sized or shorter, single-topic titles, in print format. CONCLUSIONS: Libraries have a responsibility to make both print and ebooks available to their users.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".