Demonstrating the financial impact of clinical libraries: a systematic review
Bibliographic record
Abstract
OBJECTIVE: The purpose of this review is to evaluate the tools used to measure the financial value of libraries in a clinical setting. METHODS: Searches were carried out on ten databases for the years 2003-2013, with a final search before completion to identify any recent papers. RESULTS: Eleven papers met the final inclusion criteria. There was no evidence of a single 'best practice', and many metrics used to measure financial impact of clinical libraries were developed on an ad hoc basis locally. The most common measures of financial impact were value of time saved, value of resource collection against cost of alternative sources, cost avoidance and revenue generated through assistance on grant submissions. Few papers provided an insight into the longer term impact on the library service resulting from submitting return on investment (ROI) or other financial impact statements. CONCLUSIONS: There are limited examples of metrics which clinical libraries can use to measure explicit financial impact. The methods highlighted in this literature review are generally implicit in the measures used and lack robustness. There is a need for future research to develop standardised, validated tools that clinical libraries can use to demonstrate their financial impact.
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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 | 0.028 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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, 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".