A Case Study on How Reference Staffing and Visibility Models Impact Patron Behaviors
Bibliographic record
Abstract
A Review of: Holm, C.E. & Kantor, S. (2021). Reference is not dead: A case study of patron habits and library staffing models. Portal: Libraries and the Academy, 21(2), 299–316. https://doi.org/10.1353/pla.2021.0017 Abstract Objective – To determine if reference staffing models are a predictor of reference question rates and if academic library patrons’ reference behaviors are linked to reference staffing models and desk visibility. Design – A retrospective case study. Setting – Two academic libraries at a large R3 public university in the state of Georgia, United States of America. Subjects – 10,295 service transactions (chat and in-person, including non-reference transactions related to directional and technology questions) from the 2016 fiscal year and 6,568 service transactions (chat and in-person, including only chat non-reference transactions) from FY 2017. Methods – Analysis of two years of service transaction data (July 2015 to June 2017) recorded by librarians using the reference analytics module of Springshare’s LibAnswers at three locations (virtual 24/7 chat and two libraries with different physical locations, such as centrally-located or harder-to-find service points) for three kinds of reference service modes: chat, fully-staffed in-person services, and occasional “on-call” services. “Reference” transactions were classified using the Reference & User Services Association (RUSA) definition. Email, SMS/text, and Facebook inquiries were excluded from this study. One library, which had the same service model for the 2016-2017 fiscal years, served as the study’s “control” so that an analysis of service model alterations could be conducted.Main Results – The rate of chat reference remained steady, independent from the desk model employed. There was also an overall decline in reference questions from FY 2016 to FY 2017. For FY 2016, the average daily chat transaction rate was 16.1 inquiries (range: 0 inquiries for some days and up to 51 for others) compared to an average 20.5 inquiries at the two physical service locations (range: 0 to 95 inquiries per day). In FY 2017, the average daily chat transaction rate was 13.9 inquiries (range: 0 to 46 inquiries per day) compared to 6.8 transactions for the physical locations (range: 0 to 19 inquiries per day). For FY 2016, when the model shifted to on-call, the average daily chat transaction rate was 14 inquiries compared to the physical locations with 0 and .67 inquires per day. In FY 2017, the averages were 19.33 for chat compared to .33 and .33 for the physical locations. Conclusion – For the two fiscal years studied here, question rates and reference behaviors seemed to be linked to staffing models. Patrons in this study preferred a staffed and visible desk and 24/7 chat, while “on-call” services were not favored. By replacing the visible desk with an on-call model, the library created a situation where chat was the only consistent reference service offering. As a result, patrons may have viewed the visible desk as being unreliable. The on-call service model appears to have negatively affected patron behavior since, according to the data presented, patrons’ reference needs were best met by chat and a visibly staffed desk service model.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".