Content Analysis of Reference Transactions Provides Guidance Regarding Staffing of Library Service Points
Bibliographic record
Abstract
A Review of:
 Bishop, B. W., & Bartlett, J. A. (2013). Where do we go from here? Informing academic library staffing through reference transaction analysis. College & Research Libraries, 74(5), 489-500.
 
 Objective – To identify the quantity of location-based and subject-based questions and determine the locations where those questions are asked in order to inform decision-making regarding optimal placing of staff.
 
 Design – Content analysis of location-based and subject-based reference transactions or transcripts collected using LibStats at 15 face-to-face (f2f) service points and via virtual services.
 
 Setting – Virtual and f2f service points at University of Kentucky (UK) campus libraries.
 
 Subjects – 1,852 location-based and subject-based reference transactions gathered via a systematic sample of every 70th transaction out of 129,572 transactions collected. 
 
 Methods – Using LibStats, the researchers collected data on location-based and subject-based questions at all service points at UK Libraries between 2008 and 2011. The researchers eliminated transcripts that did not include complete data or questions with fields left blank. If all question fields were properly completed, identification and coding of location-based or subject-based questions took place. 
 
 Usable transcripts included 1,333 questions that contained sufficient data. For this particular content analysis only the question type, reference mode, and location of question were utilized from the data collected. Unusable transactions were removed prior to content analysis, and reliability testing was conducted to determine interrater and intrarater reliability. Interrater reliability was high (Krippendorff’s alpha = .87%) and intrarater reliability was acceptable (Cohen's kappa = .880). 
 
 Main Results – From the usable transcripts, 83.7% contained location-based questions and 16.3% were subject-based, and a little over 80% of location-based questions and 77.2% of subject-based questions were asked face-to-face (f2f). Of the location-based questions, 11.5% were directional questions and many of these questions were related to finding places inside the libraries. “Attribute of location” questions related to library services and resources, such as finding an item, printing, circulation, desk supplies, and computer problems, made up 72.8% of total question transactions. Researchers found that subject-based questions were difficult to categorize and noted that other methods would be needed to analyze the content of these questions. Professional librarians and library staff are better equipped to answer these questions, and the location where the question asked is irrelevant. The researchers addressed the issue of where questions were asked by recording the reference mode (chat, e-mail, phone, or f2f) and location service point at UK Libraries. Overall, 79% of questions were asked f2f, rather than via chat or e-mail. Researchers think that this is due to a lack of marketing efforts regarding those services, noting that most questions were asked in the system’s large main library, which also receives the most subject-based questions. 
 
 Conclusion – This study can inform the UK Libraries system as to where their resources are most needed and allow for more strategic decision-making regarding staffing. The study could also prompt development of a mobile application to answer location-based questions, though more investigation is needed before moving forward with development of a mobile app. Due to the findings of this study, UK Libraries will deploy their professional library staff to locations where subject-based questions were most frequently asked. Because staffing of libraries is one of the “most expensive and valuable resources,” academic libraries can use this method to validate their current staffing strategies or justify the allocation of staff throughout their systems (p. 499).
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.597 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".