Key Performance Indicators in Irish Hospital Libraries: Developing Outcome-Based Metrics to Support Advocacy and Service Delivery
Bibliographic record
Abstract
Objective – To develop a set of generic outcome-based performance measures for Irish hospital libraries. Methods – Various models and frameworks of performance measurement were used as a theoretical paradigm to link the impact of library services directly with measurable healthcare objectives and outcomes. Strategic objectives were identified, mapped to performance indicators, and finally translated into response choices to a single-question online survey for distribution via email. Results – The set of performance indicators represents an impact assessment tool which is easy to administer across a variety of healthcare settings. In using a model directly aligned with the mission and goals of the organization, and linked to core activities and operations in an accountable way, the indicators can also be used as a channel through which to implement action, change, and improvement. Conclusion – The indicators can be adopted at a local and potentially a national level, as both a tool for advocacy and to assess and improve service delivery at a macro level. To overcome the constraints posed by necessary simplifications, substantial further research is needed by hospital libraries to develop more sophisticated and meaningful measures of impact to further aid decision making at a micro level.
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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 | 0.086 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".