Improving the Provision of Mental Health Care in Small-Scale Fisheries to Boost Livelihood Outcomes: A Case Study of Chilika Lagoon in India
Bibliographic record
Abstract
Small-scale fisheries (SSF) support over 90 percent of the 120 million people engaged in capture fisheries globally. Due to their strong reliance on their surroundings, SSFs are in volatile positions. The ever-changing nature leads to social, economic, and environmental vulnerabilities, such as loss of biodiversity, institutional changes and loss of income or poverty. These put millions of individuals at risk for negative impacts, which include not only physical health but also severe mental health consequences. Hence, there is a need to better understand mental health as a significant vulnerability in SSF and in turn, find solutions for improvement to the provision of care. \n \nMental health is a state of mental well-being that enables people to cope with the stresses of life and contribute to their community. It has intrinsic and instrumental value and is integral to our well-being. Although most people are resilient, people who are exposed to adverse circumstances – including poverty, violence, disability, and inequality – are at higher risk of developing a mental health condition. Poor mental health leads to detrimental outcomes for fisher’s already vulnerable livelihoods, and if untreated can lead to death. Currently, there is a lack of aid available in these communities for adequate mental health care. The purpose of this research is to discover and provide recommendations for better-supporting individuals suffering from mental illnesses in SSF. A qualitative research methods approach will be used to investigate this aspect. The administered household questionnaire will be used to gain insight into how mental health is understood by fishers, how it is influenced, the negative effects on fishers’ livelihood, barriers present in improving their mental health and the overall needs of small-scale fishers - in the study area of Chilika Lagoon, India. This design will aid in unravelling the cultural context and experiences of the people. \n \nThe implications of this research will be to discover solutions for providing better mental health care, and ultimately create a set of guiding recommendations for best practices in mental health care within SSF. The goal is to increase viable, sustainable local governance response in policy and society. Reducing poor mental health outcomes will transition small-scale fisheries from vulnerability to viability. The importance of this research is seen through its originality as there is limited literature for bridging mental health care and viability in the SSF context. Future studies should focus on ongoing efforts to understand the complex existence of mental health in small-scale fisheries, and the multidimensional response that is needed to address it. Consulting small-scale fishers to recognize their specific needs should continue to be prioritized in future solutions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".