Crisis Leadership Characteristics Necessary for Public Library Personnel During Natural Disaster Situations Include Emotional Control, Empathy, Collaborative Coordination, and Community Advocacy
Bibliographic record
Abstract
A Review of: Tu-Keefner, F., Hobbs, A., & Lyons, D. (2025). Libraries on the front lines: The imperative for disaster-ready information professionals. Journal of Library Administration, 65(1), 79–99. https://doi.org/10.1080/01930826.2024.2432230 Objective – To determine critical leadership characteristics necessary for library staff in times of crisis and disaster. Design – Thematic analysis of previous case studies that employed focus groups and interviews. Setting – Specific public libraries in South Carolina, Texas, California, and Kentucky (USA) that experienced natural disaster events between 2015-2024. Subjects – Library administrators, librarians, and staff members involved in library services during crisis situations. Methods – Transcripts from focus groups and interviews, conducted during site visits and online meetings, were thematically analyzed using Boin et al.’s (2005) five critical tasks of crisis leaders and Goleman’s (1998, 2004) five components of emotional intelligence at work. Main Results – The authors’ qualitative analysis reveals evidence of five effective crisis leadership characteristics that include: 1) self-awareness and initiative in times of crisis that result in community-first engagement initiatives; 2) the ability to maintain emotional control and empathy in order to prioritize staff and community needs; 3) goal oriented and collaborative decision making and coordination of services; 4) provision of clear and credible communication of information; and 5) learning and growing from experiences in order to make decisions in the moment but also to plan and train for future situations. Conclusion – Based on their analysis, the authors present and prioritize ten key recommendations for crisis and disaster management aimed at enhancing community engagement. The authors also recommend that LIS education programs incorporate instruction on key crisis leadership characteristics and emphasize the importance of continuing education and professional development. They highlight the need for collaborative planning efforts during non-crisis periods to ensure libraries are adequately prepared for future emergencies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".