Getting everyone on the same page: A staff focus group study for library web site redesign
Bibliographic record
Abstract
\n\t\t\t\t\tPurpose - Using staff focus groups in the redevelopment of a library web site deploys their knowledge of user navigation issues and search strategies and addresses the unique needs of library staff. This paper seeks to describe the process of planning, recruiting, and conducting staff focus groups and provide a discussion of lessons learned. Design/methodology/approach - A committee of professionals and non-professionals from the University of Calgary Library conducted a series of five focus groups with library staff. The goals were to determine their content and service priorities for the redesigned library web site, and also to ensure that staff was included in the redesign process. Findings - This paper makes recommendations for library staff focus group interviewing, including planning, formulating questions, recruitment, conducting sessions, and analysis and reporting. Practical implications - Focus group interviews can be effectively conducted in-house, with careful planning and adherence to established guidelines. Focus groups are a very useful method for gathering staff input for web site redesign or any other library-planning project. Originality/value - This paper will be useful to librarians interested in assessing staff needs and priorities through focus group interviews. The paper fills a void in the library literature regarding the use of library staff as both focus group leaders and participants.\n\t\t\t\t
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; both teacher heads agree on what is shown here.
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".