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Record W2129973888 · doi:10.18438/b81w4j

The Impact of the Acquisition of Electronic Medical Texts on the Usage of Equivalent Print Books in an Academic Medical Library

2010· article· en· W2129973888 on OpenAlexaffvenue
Pam Morgan

Bibliographic record

VenueEvidence Based Library and Information Practice · 2010
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsElectronic bookComputer scienceVendorElectronic journalCollection developmentWorld Wide WebUsage dataBusiness

Abstract

fetched live from OpenAlex

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.

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

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 armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptScholarly communication
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.054
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.012
GPT teacher head0.274
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

Scholarly communication

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2010
Admission routes2
Has abstractyes

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