Chagas disease (ChD) remains a major health burden in Latin America, and scalable screening tools are required to pre-select high-risk patients for confirmatory testing. Electrocardiograms (ECGs) are widely available, but machine learning models trained on heterogeneous datasets are vulnerable to domain bias. As part of the ‘Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025’, this paper presents a domain-aware framework using a transformer encoder initialized with released pretrained weights, combined with a four-class reformulation of SaMi-Trop and PTB-XL cohorts and a domain-adversarial head to promote domain-invariant features. In five-fold cross-validation, the model achieved an average challenge score (Recall@5%) of 0.824 (best 0.852, with AUROC/AUPRC of 0.883/0.852). Predicted probabilities remained conservative, avoiding extreme confidence while maintaining strong ranking. Submitted under the team name FjordNet, this model achieved an official test score of 0.173, ranking 31st out of 41 teams. These results demonstrate that the transformer encoder structure with multi-class adversarial training improve local robustness, but domain-aware validation and dataset diversification are necessary for generalizable ChD ECG screening.