What words and where? Applying usability testing techniques to name a new live reference service.
Bibliographic record
Abstract
OBJECTIVE: A user-focused design approach was taken to develop a new "live reference" service at the Health Sciences Library, University of Saskatchewan. The name of the service, the placement of the links, and the design of a graphical button were seen as key elements in creating service awareness and its subsequent use. METHODS: To ensure library users recognized and understood the label for the new service, selected library users were given an opportunity to choose a phrase that would best describe the service. The top two preferred phrases were then placed on the library Web pages as text and graphic images for further testing. Some pages had links in multiple locations to determine which placement worked best. Task-based usability testing was carried out with participants who were unaware of the new service. Participants were observed as they completed seven Website tasks arranged in increasing levels of difficulty to see whether they would notice the live reference service and seek assistance. RESULTS: The high level of recognition and use of the service indicate that the label name and link placement were effective with library Website users. CONCLUSIONS: Using user-centered design methodology helped ensure that the new live reference service was visible and used and demonstrated the effectiveness of the user-centered design approach for adding new services to an existing Website.
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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 | 0.015 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".