Impact of Non-Conventional Water Use Development on the Well-Being of Refugees in Jordan
Bibliographic record
Abstract
This study aims to explore the dimensions of non-conventional water (NCW) resources, including wastewater treatment and greywater, and their impact on the well-being of refugees in the context of Alzatari refugee camps in Jordan.The research employs the theory of planned behavior (TPB) as a conceptual framework.To look into the connections between NCW, attitudes, subjective norms, and the well-being of public health, education, the environment, and income, the study uses statistical methods like descriptive analysis with SPSS, confirmatory factor analysis (CFA), and mediation analysis with SEM-AMOS.The findings demonstrate that attitudes and subjective norms play mediating roles in the relationship between NCW and the well-being of refugees.The study highlights the importance of addressing these factors, as they significantly influence decision-making processes and perceptions of NCW.Additionally, the analysis reveals that income well-being do not exhibit a significant relationship in the context of refugee camps.Where mental health was the most affected factor.In conclusion, the analysis of refugee attitudes supports the theoretical conclusion of the TPB model, which emphasizes the interaction between well-being and sustainable technological characteristics.These insights contribute to our understanding some of water technology development requirements in vulnerable regions and facilitate the sustainable management of water resources in refugee camps.The study provides valuable information for the development of strategies to address water scarcity while safeguarding the well-being and livelihoods of refugees in asylum areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".