College Students and Environmental Disasters: A Review of the Literature
Bibliographic record
Abstract
College students are a unique population occupying a distinct life-course and transition period between adolescence and adulthood. Although not monolithic in experiences, knowledge, and demographics, this diverse population is particularly susceptible to immediate, short-term, mid-term, and long-term disaster impacts. Recently, disaster research focusing on college students has rightly focused on the ongoing COVID-19 pandemic. Although the pandemic was a public health disaster interrupting social, developmental, and educational processes for students on a global scale, the climate crisis and related environmental disasters continuously threaten college students’ individual development, health, and well-being. Thus, it is critical to understand current knowledge focusing on environmental disasters and college students in order to determine future research needs. This article used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to examine research on college students and disasters over the past ten years (2014–2023). We identified 67 articles, which we analyzed through a mixed methods approach, including descriptive statistics and thematic analysis. Results indicate that disaster impacts on college students are an understudied topic in the social sciences, especially in an era of more-frequent and -intense environmental hazards. Our findings demonstrate a need to engage college students in disaster research worldwide so that trade schools, colleges, and universities can collaborate with policymakers to build this unique and disproportionately impacted population’s capacity to mitigate against, respond to, and recover from environmental hazards in an ever-changing climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".