<![CDATA[Good governance, evidence-based planning, and sustainable rural development all rely on correct rural public service performance predictions. This study presents a Cross-Attention Multimodal Transformer developed for binary time-series classification of service conditions in the areas of agriculture, health, and environment at the level of the local government unit (LGU). Using bidirectional cross-attention layers, the model mixes several temporal signals so that healthcare and agriculture-environment streams can interact with one another. Using weighted uncertainty and calibration-awareness in a loss function helps to guarantee that the confidence scores are properly calibrated. With AUCs of 83.00% (agriculture), 79.40% (environment), and 63.90% (healthcare), which is lower, experimental results on a rural public service dataset indicate great discriminative and calibration performance. With 61.50%, 23.10%, and 18.90% respectively, the Brier scores suggest that the forecasts for health care and the environment are well calibrated. These findings suggest that cross-attention multimodal transformers may be quite useful in producing precise binary predictions of rural service results. At the LGU level, this would enable data-driven decision-making support.]]>
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