Climate change: A pointer to increased small-scale fisher drowning deaths
Bibliographic record
Abstract
Drowning is an overlooked public health concern and drowning risk is dependent on environmental risk factors. The preponderance of drowning deaths occurs in low- and middle-income countries. Small-scale fishers face high occupational risk of drowning. Climate change increases the frequency and intensity of storms, thereby exacerbating fishers' risks and creating a need to examine the contribution of storms to fisher drowning deaths for the development of mitigation strategies. We examined this relationship between weather and fisher drowning deaths in Lake Victoria, which is Africa's largest lake, a site of high fishing pressure, and where climate change is predicted to increase thunderstorms. We conducted a verbal autopsy with people knowledgeable about recent fatal fisher drowning incidents to collect information about the deceased fishers and circumstances surrounding the incidents across 43 landing sites in the Kenyan shore of Lake Victoria. Semi-structured interviews with stakeholders also elucidated community perspectives on drowning risks. Fatal drownings were often attributed to bad weather (41.8%). Other risk factors, such as non-use of life jacket and navigation equipment, co-occurred with bad weather at high rates (69.5% and 67.8%, respectively) to jointly contribute to fatal drowning incidents. Such co-occurrence of risk factors indicates that actions across multiple risk factors can help mitigate the issue. Stakeholder analysis revealed a range of opportunities for improved communication of risks and action to mitigate risks across boat operators and manufacturers, as well as multiple levels of management. Across global small-scale fisheries, limited use of safety equipment and intensive fishing pressure may coincide with increases in extreme weather events, necessitating action to address current and mitigate future drowning risks to small-scale fishers.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".