Reconnoitering NGOs strategies to strengthen disaster risk communication (DRC) in Pakistan: A conventional content analysis approach
Bibliographic record
Abstract
Risk communication is crucial since individuals need to understand how they are at risk and what proper steps to deal with flood events. Sharing information with the public opens the door for two-way communication about risks, wherein you learn about people's perspectives and work together to find ways to mitigate the risks. Beyond government scope, relief organizations play a big part in advising individuals about the likelihood of catastrophic events as they possess the commonalities that define community engagement. In numerous accounts of devastating events, the failure of risk management groups to coordinate their efforts and the public's mistrust of relief agencies are highlighted. One possible explanation for this skepticism could be relief organizations' failures in communicating risks. In addition, individuals' lack of skills and experience with catastrophes has left rural residents unprepared, which is why relief agencies need to raise their efforts or measures to communicate with people about possible risks. If these measures are uncovered, it could improve public communication and provide information for formulating recommendations to prevent fatalities. This study identifies the strategies used by relief organizations in enhancing disaster risk communication across four severely affected districts in Khyber Pakhtunkhwa, Pakistan. The qualitative research used semi-structured interviews with 50 participants from relief organizations, local institutions, and affected households. We employed qualitative content analysis and NVivo software to analyze the data. The findings of this study highlighted some significant strategies that relief organizations adopted in this line of work: the administration of educational and information transmission, managing obstacles in communication, and managing inter-organizational communications. The findings validate the potential for relief organizations to become change agents, facilitate communication between the public and relief organizations, and ultimately strengthen community resilience and reduce disaster risks as part of local responses.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".