Bibliographic record
Abstract
Purpose The purpose of this paper is to compare user perspectives on visits to in‐person and virtual reference services conducted by participants in the Library Visit Study, an ongoing research project. Design/methodology/approach This paper compares satisfaction rates, identifies staff behaviours that influence user satisfaction, and suggests how both face‐to‐face and virtual reference can be improved. Since 1990, participants in the Library Visit Study have been MLIS students who ask questions at in‐person and virtual reference desks, and report on their experiences. In addition to these accounts, students complete questionnaires on their experiences. Level of satisfaction with the in‐person or virtual transactions, based on the “willingness to return” criterion, are computed. Satisfaction is compared with other factors such as correctness of answers and friendliness of library staff. Underlying problems that influence satisfaction are identified. Findings – Data from 261 in‐person and 85 virtual reference transaction accounts (both e‐mail and chat) show that virtual reference results in lower satisfaction than in‐person reference. Underlying problems that are associated with user dissatisfaction were identified in face‐to‐face reference and carry over to virtual reference, including lack of reference interviews, unmonitored referrals and failure to follow‐up. Research limitations/implications – The number of virtual reference visits is relatively small (85) compared with 261 in‐person visits. Practical implications – The reasons for ongoing failures are examined and solutions that can help improve both face‐to‐face and virtual reference are identified. Education and training of reference staff can be improved by recognition of the behavioural causes of dissatisfaction in users. Originality/value – This paper provides empirical data that compare user perceptions of in‐person and virtual reference.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".