Automated feedback is increasingly cited as a key advantage of AI-based psychotherapy training, yet the clinical groundedness of LLM-generated supervisory feedback remains unevaluated. We present an expert evaluation of supervisory feedback generated by PRACTICE, an LLM-powered open-ended psychotherapy training simulator, across 21 feedback instances from four novice trainees. Two clinical psychology experts independently coded 167 feedback propositions as Justified, Unjustified, or Unsure. Inter-rater reliability was near-perfect (raw agreement = 98.2\%; $\kappa$ = 0.902). Of the 167 propositions, 149 (89.2\%) were rated Justified; however, 52.4\% of feedback instances contained at least one non-justified proposition, and qualitative analysis identified three recurring failure types: incorrect characterization, referential imprecision, and unclear communication. In clinical training contexts, even low error rates carry ethical weight: unjustified feedback risks reinforcing inappropriate clinical behaviors in trainees that can be trasnferred to real practice. These findings provide an initial empirical basis for the responsible deployment of LLM-generated feedback in clinical training and call for traceable, expert-auditable feedback architectures.