Bibliographic record
Abstract
Objective: The Penn State College of Medicine and Penn State Health have separate medical libraries and changes have made it difficult to determine who has access to either library and their very different collections.What strategies have other health sciences libraries implemented to provide/extend equitable access to the library information resources and services to affiliated health system members and which of the identified strategies would be most applicable to the research teams' institution?Methods: This project utilizes survey data collected in 2022 from health sciences libraries across North America.That survey and subsequent publication were focused on the challenges and opportunities for libraries in relation to fast growing academic health systems.Additionally, an updated literature search will be conducted.Results: From this survey data set, the research team has identified relevant questions focused on what educational groups and hospital/health systems do the libraries serve and what strategies are used to integrate hospital/health systems to provide services and access to library information resources.The team will focus on these questions and analyze responses to identify patterns in how health sciences libraries provide services to health systems and related academic institutions.Discussion: The analysis will include reviewing both common and novel approaches that could be applicable to the research teams' intuitional structure.The final step of this research project is to identify and select up to two service/access models for the library administration to advocate as the best for the users, the library, and the systems. PP2. Are patrons "clicking" with the library's literature search service? Assessing patron engagement using Short.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.678 | 0.417 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".