Demonstrating library value: the development of a customizable Library Value Planner
Bibliographic record
Abstract
Background: Library professionals in the health sciences sector need to strategically plan and map out library services. Each library and their parent organization have unique needs and service offerings. Objective: To develop an adaptable Library Value Planner (LVP) tool based on the Levels of Library Service benchmarking document developed by the Health Science Information Consortium (HSIC) that can be used for (i) strategic and operational planning and (ii) mapping out needs for implementing new library services in individual contexts. Methods: This project involved: (i) searching the literature; (ii) analyzing current trends and best practices in Canadian health libraries; (iii) updating and renaming of the Levels of Library Service document; (iv) drafting and disseminating a French and English survey; (v) leading French and English focus groups; (vi) analyzing the feedback received from the surveys and focus groups, and (vii) revising the tool based on this feedback. Results: The results from the surveys and the focus groups showed that participants were satisfied with the versatile nature of the LVP. Some respondents expressed concerns about the formatting of the LVP and others were not sure how and when the LVP ought to be used. This feedback highlighted the need to develop and disseminate education for library professionals about the tool. Conclusion: The CHLA/ABSC Standards Standing Committee developed a flexible and robust tool that, when paired with education, can be used to advocate and demonstrate the value of library services in the health sciences.
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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 | 0.050 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".