Health Sciences Patrons Use Electronic Books More than Print Books
Bibliographic record
Abstract
A Review of: Li, J. (2016). Is it cost-effective to purchase print books when the equivalent e-book is available? Journal of Hospital Librarianship, 16(1), 40-48. http://dx.doi.org/10.1080/15323269.2016.1118288 Abstract Objective – To compare use of books held simultaneously in print and electronic formats. Design – Case study. Setting – A health sciences library at a public comprehensive university with a medical college in the southern United States. Subjects – Usage data for 60 books held by the library simultaneously in print and electronically. The titles were on standing order in print and considered “core” texts for clinical, instructional, or reference for health sciences faculty, students, and medical residents. Methods – Researchers collected usage data for 60 print titles from the integrated library system and compared the data to COUNTER reports for electronic versions of the same titles, for the period spanning 2010-2014. Main Results – Overall, the 60 e-book titles were used more than the print versions, with the electronic versions used a total of 370,695 times while the print versions were used 93 times during the time period being examined. Conclusion – The use of electronic books outnumbers the use of print books of the same title.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".