Assessing reference services training through student staff satisfaction
Bibliographic record
Abstract
Most research on satisfaction with academic library reference services explores patron, not staff, satisfaction. The research team speculated that understanding staff satisfaction with reference interactions could be used as a tool to help determine the proficiency of reference staff training. During the academic school years of 2019–2020 and 2022–2023, student reference desk staff at the University of Toronto’s Engineering & Computer Science Library were asked to complete a survey after each reference interaction. The survey questions assessed both staff satisfaction and their perception of patron satisfaction against variables such as time of year, patron time constraints, number of questions asked, type of question(s), and patron affiliation. Results showed that staff were satisfied with most interactions, however, they would benefit from reminders that information may not exist for some reference questions, and staff should not internalize this or be hard on themselves. In interactions where staff expressed the most dissatisfaction, ambiguity in any form appeared to affect staff satisfaction more than any of the other factors. Moving forward, exploring how other fields develop training and strategies to help staff manage high levels of on-the-job ambiguity could be applied to better support library reference staff training.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".