An Analysis of User Complaints on Chat Reference during the COVID-19 Pandemic: Insights into User Priorities
Bibliographic record
Abstract
This study aims to identify patterns in user complaints within chat transcripts before and during the COVID-19 pandemic. The researchers analyzed 3,339 chat transcripts from an academic chat reference consortium between January 2019 to December 2021. Transcripts were hand-coded for pandemic status, semester, user type, presence of a complaint, complaint type and subtype, and complaint resolution. We tested the significance of relationships between variables using Pearson’s chi-square test of independence and Fisher’s exact test. Over the three-year study period, 17.6% of chats contained at least one complaint, with faculty and graduate students complaining more than expected, and undergraduates complaining less than expected. The most common complaint types concerned e-resources, accounts, and research. There were significant differences in complaints according to user type: students and faculty complained more about e-resources, staff and alumni complained more about accounts, and members of the public complained more about policies. The researchers found a statistically significant increase in complaints during the pandemic, with more complaints about document delivery and borrowing and e-access eligibility than expected, and fewer complaints about library accounts and noise than expected. Most complaints across the study period were resolved, typically via referral.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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, 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".