Bibliographic record
Abstract
Objective - To describe the key elements of cost effectiveness analysis (CEA) and demonstrate how such analysis may be used in the library environment. Methods - The paper uses a step by step approach to walk the (non-economist) reader through the basics of conducting a cost effectiveness study. The key elements of a CEA are outlined using examples that illustrate how the analysis may be carried out in the library sector. A case study of a CEA in a hospital library is presented. The case study compares two library services, mediated searching and information skills training, to illustrate the application of CEA and highlight some of its limitations. Results - CEA is a comparative analysis; its key elements include a study question that includes both costs and effectiveness; justification of the perspective the study; evidence of the effectiveness; comprehensive identification of all relevant costs and appropriate measurement of costs and effectiveness. Conclusions - CEA enables comparison of services or interventions in terms of their costs and how effective they are. The results can be used to aid decision-making about service provision.
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 | 0.020 | 0.079 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.071 | 0.026 |
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".