The Disaster Information Specialist: An Emerging Role for Health Librarians
Bibliographic record
Abstract
This article describes the emergence of disaster information (DI) specialists, with particular focus on their presence in health libraries. Although literature on the subject of disasters and libraries is dominated by accounts of librarians preserving collections and ensuring continuity of library operations following a flood, fire, or other disaster event, the work of DI specialists extends beyond these traditional roles. DI specialists conduct outreach in the community, providing information services to emergency managers and other disaster workers. This article recounts a history of disaster information service in which public librarians served communities during disaster recovery periods, and health librarians became involved in organizational disaster planning activities. DI products from the National Library of Medicine are introduced in addition to federal funding opportunities for DI outreach projects. The development of the Medical Library Association's Disaster Information Specialization Program is presented, and the article shares recommendations for library administrators to encourage DI training for librarians and support the development of outreach services to disaster workers.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".