Lessons from the 2015 earthquake(s) in Nepal: implication for rehabilitation
Bibliographic record
Abstract
PURPOSE: There has been an increase in the number of natural disasters in recent history, and the rate of disability is increasing among survivors. The most recent major natural disaster was the earthquake(s) that occurred in Nepal on 25 April 2015 and 12 May 2015. In total, more than 8500 people were killed and over 18,500 people were left injured. This article aims to demonstrate the role of rehabilitation professionals in post-disaster relief and beyond in Nepal. METHOD: This is an experiential account of physiotherapists present during the earthquake and participating in the post-disaster relief. RESULTS: Rehabilitation professionals played an important role in the acute phase post-disaster by providing essential services and equipment. However, discharge planning emerged as an important role for rehabilitation providers in the early days of post-disaster and signaled a relatively new and innovative function that facilitated the heavy imbalance between little supply and tremendous demand for care. In the coming years, rehabilitation will need to support local initiatives that focus on minimizing the long-term effects among people with a newly acquired disability. CONCLUSIONS: Rehabilitation serves an important role across the continuum in post-disaster relief from the initial stages to the months and years following an event. IMPLICATIONS FOR REHABILITATION: Driven by medical advances in acute field medicine, the relative proportion of casualties following natural disasters is decreasing, while relative rates of disability are rising among survivors. In post-disaster settings, the growing number of people with newly acquired disabilities will be added to the existing proportion of the population who lived with disabilities, creating a significant growth in the total number of people with disabilities (PWDs) in communities that are often ill prepared to provide necessary services. Rehabilitation interventions in the initial stages of emergency humanitarian response can minimize the long-term effects among people with newly acquired disabilities through early activation and prevention of secondary effects. Rehabilitation providers thus appear to have an important mediating effect on outcomes of disabilities in the early stages, but must also be strong partners with PWDs to advocate for social and political change in the long term.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".