Beyond Reference Data: A Qualitative Analysis of Nursing Library Chats to Improve Research Health Science Services
Bibliographic record
Abstract
Objective - The objective of this study was to analyze trends in academic library reference chat transcripts with nursing themes, in order to improve all library services and resources based on the findings. Methods - In Fall 2018, health science liaison librarians performed a qualitative study by analyzing 60 nursing chat transcripts from LibraryH3lp. These chats were tagged, anonymized, coded, and then analyzed in Atlas TI to identify patterns and trends. Results - Chat analysis showed that librarians staffing chat are meeting the research needs of nursing patrons by helping them find full-text articles and suggesting the appropriate library databases. In order to further improve these virtual services, workshops were offered to Library and Information Science (LIS) interns and staff who answer reference chats. Nursing online tutorials and research guides were also improved based on the results. Conclusion - This study will help academic libraries improve and expand services into the virtual realm, to support library employees and patrons during the COVID-19 pandemic and beyond. Virtual reference chat is not going away; in the current academic environment it is needed more than ever. Using these library chats as the basis for additional chat staff training can reduce staff anxiety and prepare them to better serve patrons.
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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 | 0.026 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".