Detecting psychological defense mechanisms in therapy dialogue is a clinically valuable but computationally underexplored task. We present our systematic analysis for PsyDefDetect, a shared task at BioNLP@ACL 2026, which frames defense detection as a nine-class utterance-level classification problem based on the Defense Mechanism Rating Scale (DMRS). We systematically evaluate six open-source, instruction-tuned small language models (SLMs, = 9B parameters) in zero-shot and fine-tuning settings, and compare a clinically-grounded prompt against the organizer-provided baseline. Our official submission achieved 59.96% accuracy and 16.28% Macro F1. Post-submission experiments show that fine-tuning combined with 5-fold cross-validation and logit averaging ensemble substantially improves performance, with the best configuration reaching 34.59% Macro F1 and 65.25% accuracy. We find that clinically-grounded prompts outperform bare label definitions, model scale does not consistently improve zero-shot performance, and fine-tuning dramatically recovers even collapsed zero-shot models. Certain defense tiers remain persistently difficult across all settings, pointing to clinical ambiguity at tier boundaries as a more fundamental bottleneck than data imbalance alone.