Identifying and Classifying User Typologies Within a United Kingdom Hospital Library Setting: A Case Study
Bibliographic record
Abstract
Objective – To identify available health library user typology classifications and, if none were suitable, to create our own classification system. This is to inform effective future library user engagement and service development due to changes in working styles, information sources and technology.
 
 Methods – No relevant existing user typology classification systems were identified; therefore, we were required to create our own typology classification system. The team used mixed methods research, which included literature analysis, mass observation, visualization tools, and anthropological research. In this case study, we mapped data across eleven library sites within NHS Greater Glasgow and Clyde Library Network, a United Kingdom (U.K.) hospital library service.
 
 Results – The findings from each of the NHS Greater Glasgow and Clyde Library Network’s eleven library sites resulted in six user typology categories: e-Ninjas, Social Scholars, Peace Seekers, Classic Clickers, Page Turners and Knowledge Tappers.
 
 Each physical library site has different profiles for each user typology. The predominant typology across the whole service is the e-Ninjas (28%) with typology characteristics of being technically shrewd, IT literate and agile – using the library space as a touch down base for learning and working.
 
 Conclusions – We identified six distinct user types who utilize hospital library services with distinct attributes based on different combinations of library activity and medium of information exchange. The typologies are used to identify the proportional share and specific requirements, within the library, of each user type to provide tailored services and resources to meet their different needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.406 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".