Diffusion of new technology, health services and information after a crisis: a focus group study of the Sichuan “5.12” Earthquake
Bibliographic record
Abstract
INTRODUCTION: The Sichuan "5.12" Earthquake in 2008 occurred in a relatively underdeveloped area in China. The rainy weather, the mountainous environment and the local languages all posed major challenges to the dissemination of information and services after the disaster. By adopting a communication perspective, this study applies the diffusion of innovations theory to investigate how healthcare professionals diffused health technologies, health information and services during the rescue and relief operation. METHOD: The authors conducted three focus group sessions with the health professionals who had attended to the rescue and relief work of the Sichuan "5.12" Earthquake in 2008. A range of questions regarding the diffusion of innovations were asked during these sessions. RESULTS: The health professionals used their cell phones to communicate with other healthcare providers, disseminated knowledge of health risks and injuries to affected residents with pamphlets and posters and attended daily meetings at the local government offices. They reported on the shortage of maritime satellite cell phones and large-size tents for medical use, and the absence of fully equipped ambulances. Volunteers, local health professionals and local officials provided health information and services in different ways. However, the diffusion of health information and services was less likely to reach those living next to transportation centers, in remote areas and in disaster areas neglected by the media. DISCUSSION: New communication devices such as cell phones and the mobile Internet enabled medical professionals to coordinate the rescue and relief work after this major natural disaster, at a time when the country's emergency response system still had plenty of room for improvement. In future, the mobile Internet should be used as a means of collecting bottom-up disaster reports so that the media will not neglect any disaster areas as they did during the Sichuan Earthquake. Rescue relief work would have been substantially easier if medical teams had been equipped with advanced appliances such as maritime satellite cell phones. "Disaster medicine" should be treated as a separate discipline in medical schools and receive more investment. Moreover, a stronger public health emergency response system is needed for more efficient dispatch and coordination.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".