Perceived Health Hazards of Low-Quality Irrigation Water in Vegetable Production in Morogoro, Tanzania
Bibliographic record
Abstract
<p>This study assessed the perceptions of vegetable farmers, traders, consumers and key informants on the health hazards of using low-quality water in irrigation vegetable production in Morogoro, Tanzania. Methods used to collect data were a survey involving all farmers in Changarawe village and Fungafunga area using low-quality water for irrigation vegetable production (n=60), consumers of low-quality water irrigated vegetables (n=70) and vegetable traders selling low-quality water irrigated vegetables (n=60), focus group discussions (n=7) and key informant interviews (n=25). The study employed cross sectional research design. Descriptive statistics were used to calculate mean, frequencies and percentages while Mann-Whitney U-test and Kruskal-Wallis H-test assessed the association between social-demographic variables and respondents score on the health hazard perception scale of using low-quality water in vegetable production. Results showed skin itching, fungal diseases, bilharzias and worm infestation as among the perceived health hazards in using low-quality irrigation water. Health hazard perception differed among groups of farmers, consumers and vegetable traders (<em>p&lt;</em>0.001). The mean ranks of the groups indicated that farmers perceive less health hazards in using low-quality water (mean rank = 147.98) compared to consumers (mean rank = 72.68) and vegetable traders (mean rank 69.64). More health hazards were perceived by Fungafunga farmers compared to farmers from the Changarawe village (<em>p&lt;</em>0.001) while female farmers perceived less hazards in using low-quality water than male farmers <em>(p </em>&lt; 0.05). Consumers with formal education perceived more health hazards than consumers with no formal education (<em>p</em> &lt; 0.001) while vegetable traders from Fungafunga area perceived more health hazards in selling low-quality water irrigated vegetable than vegetable traders from the Changarawe village (<em>p&lt;</em>0.001). These findings demonstrate the need to design health hazards minimization interventions for specific target group. </p>
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".