Libraries Demonstrate Low Adherence to Virtual Reference Service Guidelines
Bibliographic record
Abstract
A Review of: Shachaf, Pnina, and Sarah M. Horowitz. “Virtual Reference Service Evaluation: Adherence to RUSA Behavioral Guidelines and IFLA Digital Reference Guidelines.” Library & Information Science Research 30.2 (2008): 122-37. Objectives – This study evaluates the level to which virtual (asynchronous e-mail) reference services adhere to professional guidelines. Specifically, it addresses the following research questions: 1) To what extent do virtual reference services adhere to the American Library Association (ALA) Reference and User Services Association (RUSA) and the International Federation of Library Associations (IFLA) guidelines? 2) How does the level of adherence to RUSA or IFLA guidelines vary based on request type, user name, and institution? 3) Is there a correlation between outcome measures of reference transactions (accuracy, completeness, and satisfaction) and the level of adherence to RUSA or IFLA guidelines? Design – Unobtrusive evaluation of researcher-generated queries. Setting – Fifty-four academic libraries in North America. Subjects – A total of 324 queries were sent to the 54 libraries, with each library receiving six different types of requests from six different user names. Methods – Researchers developed two coding schemes for the guidelines (34 codes and 12 attributes for the RUSA guidelines and 33 codes and 10 attributes for the IFLA guidelines). Each of the six user names used represented an ethnic and/or religious group identity: Mary Anderson (Caucasian, Christian), Moshe Cohen (Caucasian, Jewish), Ahmed Ibrahim (Arab), Latoya Johnson (African American), Rosa Manuz (Hispanic), and Chang Su (Asian). The six request types were designed so that three would be answered (questions 1-3) and three would be out of scope and not answered (questions 4-6). The following queries were sent, individualized for each institution: 1) Dissertation query; 2) Sports team query; 3) Population query; 4) Subject query; 5) Article query; 6) Request for a PDF copy. The 324 queries were uploaded into NVivo 2 software, and all e-mail transactions were coded and analyzed. Main Results – Analysis of the 324 transactions from 54 libraries showed the following results: 1) Low levels of adherence to both sets of guidelines; 2) Varied levels of adherence based on request types and user names on both sets of guidelines; 3) Variation in institutional rank according to different sets of guidelines; 4) No correlation between user satisfaction and adherence to either set of guidelines. Conclusion – This study suggests that higher levels of virtual reference service effectiveness could be achieved by automatically integrating some less observed behaviours (e.g., thank you notes) into replies sent to users and by increasing librarians’ awareness of professional guidelines through training and detailed institutional policies. The authors also suggest that librarians should be aware of their tendencies to react differently to different user groups, and that administrators can facilitate this by providing diversity workshops.
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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 | 0.024 | 0.193 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".