Integrating Real-Time Feedback of Outcome Assessment for Individual Patients in an Inpatient Psychiatric Setting
Bibliographic record
Abstract
Routine assessment of psychiatric patient outcomes is rare, despite growing evidence that feedback to clinicians and patients concerning patient progress improves treatment outcomes. The authors present a case in which real-time feedback proved beneficial in the treatment of a woman with a personality disorder admitted for inpatient treatment due to worsening depression, anxiety, severe suicide risk, and decline in functioning. During the course of her 10-week hospitalization, she completed standardized assessments of symptoms/functioning at admission, at 2 week intervals, and at discharge. The distinctive feature of this case is the way in which real-time feedback to the treatment team, psychiatrist, and patient exposed hidden treatment barriers. In the midst of an improving profile with decreasing symptom severity, the patient experienced a spike in distress and symptoms, prompting her treatment team to examine the treatment plan and to engage the patient around understanding the decline in functioning. This intervention revealed a replay of a familiar pattern in the patient's life that led to the identification and repair of a rupture in the therapeutic alliance and to an improvement in the patient's functioning. This case expands on previous research concerning the integration of individualized assessments into outpatient treatment and it illustrates the need to extend outpatient research to inpatient settings.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".