The Impact of the Acquisition of Electronic Medical Texts on the Usage of Equivalent Print Books in an Academic Medical Library
Bibliographic record
Abstract
Objectives – This study examines whether acquiring a text in electronic format effects the usage of the print version of the text, focusing specifically on medical texts. Studies in the literature dealt specifically with general collections and it was not clear if they were applicable to medical collections. It was also not clear if these studies should play a role in determining whether a medical library should purchase electronic texts or whether reserve collections are still needed for print texts. Methods – Four usage studies were conducted using data from the circulation system and the electronic vendor systems. These were 1) trends of print usage; 2) trends of electronic usage; 3) a comparison of electronic usage with print usage of the same title in the reserve collection; 4) a comparison of electronic usage with print usage of the same title in the general collection. Results – In comparison to print, substantial usage is being made of electronic books. Print is maintaining a level pattern of usage while electronic usage is increasing steadily. There was a noticeable difference in the usage levels of the electronic texts as regards to the package in which they are contained. Usage of print texts both on reserve and in the general collection has decreased over time, however the acquisition of the electronic version of a medical title had little impact on the usage of the equivalent print version. Conclusion – There is a demand for medical texts in medical libraries. Electronic versions can replace print versions of texts in reserve. Further investigation is needed of current patterns of print collection usage, with particular emphasis on trends in reserve collection usage.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.004 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".