The Use of Search Request Forms Can Identify Gaps in a Consumer Health Library Collection
Bibliographic record
Abstract
A Review of: Giannopoulos, E., Snow, M., Manley, M., McEwan, K., Stechkevich, A., Giuliani, M. E., & Papadakos, J. (2021). Identifying gaps in consumer health library collections: A retrospective review. Journal of the Medical Library Association: JMLA, 109(4), 656–666. https://doi.org/10.5195/jmla.2021.895 Abstract Objective – The objective of this study was to determine if search request forms, which are used when a patron’s request for information cannot be fulfilled at the time of contact with the library team, can be used to identify gaps in consumer health library collections while offering some explanation for the gaps. Design – Retrospective case study of search request forms. Setting – A consumer health library at an academic cancer center in Canada. Subjects – Library patrons: Patients, Patient family, other members of the center, and unspecified. Methods – The researchers reviewed 260 search request forms submitted between 2013 and 2020. Of those, 249 records met inclusion criteria and were analyzed and coded. Coding included patron type, cancer diagnosis, information delivery, and content themes. This information was then used to identify gaps in the library collection and the reasons for the gaps. Main Results – Patients were the primary patrons, asking 62.9% of the questions, followed by family members at 22.5%. The most common cancer type researched was breast at 23.3%, then hematology at 16.5%. gynecology, gastrointestinal, genitourinary, and sarcoma were next between 10% and 8.4%. The remaining cancer types ranged between 6.0 % and 2.0%, with brain being the lowest. Of the questions asked, 60% revealed a gap in the collection. The gaps included rare cancer diagnosis, treatment options, and prognosis. There were data collected on why the information was unavailable. While 53% of the gaps were a result of limited health consumer information, 25% were a result of paywall restrictions or content restricted to members. Conclusion – Search request forms can be an effective tool in evaluating gaps in collections. In this study, the researchers were able to identify that breast cancer patients made up the most significant proportion of patrons, and the biggest gaps in the collection were related to their treatment decisions. One opportunity to bridge this gap is through collaboration with clinical teams in developing patient friendly resources on this topic. In addition, inter-institutional collaboration between libraries may also help. Continued review of forms can help inform collection decisions to better meet the needs of 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 | 0.182 | 0.470 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.026 | 0.032 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".