What criteria do consumer health librarians use to develop library collections? a phenomenological study
Bibliographic record
Abstract
OBJECTIVES: The criteria for determining whether resources are included in consumer health library collections are summarized in institutional collection development policies (CDPs). Evidence suggests that CDPs do not adequately capture all of these criteria. The aim of this study was to describe the resource review experience of librarians and compare it to what is described in CDPs. METHODS: A phenomenological approach was used to explore and describe the process. Four consumer health librarians independently evaluated cancer-related consumer health resources and described their review process during a semi-structured telephone interview. Afterward, these librarians completed online questionnaires about their approaches to collection development. CDPs from participating libraries, interview transcripts, and questionnaire data were analyzed. Researchers summarized the findings, and participating librarians reviewed results for validation. RESULTS: Librarians all utilized similar criteria, as documented in their CDPs; however, of thirteen criteria described in the study, only four were documented in CDPs. CONCLUSIONS: CDPs for consumer health libraries may be missing important criteria that are considered integral parts of the collection development process. IMPLICATIONS: A better understanding of the criteria and contextual factors involved in the collection development process can assist with establishing high-quality 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 | 0.042 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".