Identifying gaps in consumer health library collections: a retrospective review
Bibliographic record
Abstract
BACKGROUND: 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. CASE PRESENTATION: Search request forms were collected from 2013 to 2020 and analyzed independently by two reviewers. Search request forms were included if they were complete and contained a record of how the request was fulfilled. Descriptive statistics were used to summarize patron characteristics. Search request forms were iteratively coded to identify themes in the data and determine if resources provided to patrons could be found within the library collection. The study team subsequently reviewed search request forms to determine reasons for identified gaps. Two hundred and forty-nine search request forms were analyzed. Six main content themes were identified: 1) understanding the cancer diagnosis, 2) cancer treatments, 3) understanding disease prognosis, 4) support during and after treatment, 5) natural health products and therapeutic effects in oncology, and 6) research literature. The majority of patrons were patients (53%). Over half (60%) of the submitted search request forms reflected collection gaps, and many (16%) contained queries for information about rare cancer diagnoses. The main reason that queries could not be satisfied was that there was limited consumer health information on the requested topics (53%). CONCLUSIONS: Search request forms are a useful resource for assessing gaps in consumer health library collections.
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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 | 0.049 | 0.212 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.028 | 0.036 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".