Individualized Research Consultations in Academic Libraries: A Scoping Review of Practice and Evaluation Methods
Bibliographic record
Abstract
Abstract Introduction – Librarians in academic institutions have been providing personalized services to the student population by offering individualized research consultations (IRC) for decades. These consultations usually consume many hours of librarians’ busy schedules, and yet the impact of these consultations is unknown. Therefore, it’s worth asking the question: what assessment methods have been used in academic libraries to evaluate the impact of IRC? Methods – A retrospective scoping review of the literature was performed using the following databases: Library and Information Science Abstracts (LISA), Educational Resources Information Center (ERIC), Library and Information Technology Abstracts (LISTA), Scopus, and Web of Science. Additionally, a manual search of the included papers reference lists was conducted to locate additional relevant papers. Articles that mentioned a format of evaluation or assessment and were based within a library setting were included. Articles that discussed group instruction that were not in a library setting, or that did not include any form of evaluation or assessment, were excluded. Results – Researchers located 578 articles and reviewed titles and abstracts. 523 titles were eliminated, while full text sources of the remaining 55 were examined to check inclusion and exclusion criteria. 20 articles remained for qualitative synthesis. Specific methods of assessment were reviewed and three overall assessment methods were identified: 1) usage statistics, 2) survey, and 3) objective quantitative methods. Conclusion – Many articles using a usage statistics method stated that they wanted to further their assessment of individual consultations. Several authors using a survey method described the value of the information gathered by surveying their users for improving their service, but also mentioned that this method is subjective in nature. They mentioned that objective assessment methods would provide a better understanding of the impact of IRCs. The few articles using objective quantitative methods obtained mixed results. Overall, more research in the assessment of IRCs is needed, particularly those with objective quantitative methods.
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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 | 0.206 | 0.396 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.041 | 0.043 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".