Patient Satisfaction with Outpatient Psychiatric Treatment: The Role of Diagnosis, Pharmacotherapy, and Perceived Therapeutic Change
Bibliographic record
Abstract
OBJECTIVE: To investigate the influence of diagnosis, type of treatment, and perceived therapeutic change on patient satisfaction following psychiatric treatment for nonpsychotic, nonsubstance-related disorders. METHOD: We mailed questionnaires, including Larsen's Client Satisfaction Questionnaire and Grawe's Bern Inventory of Treatment Goals, to outpatients who had undergone 8 or more therapy sessions 1 year following treatment. RESULTS: Patients with somatoform, eating, and personality disorders were less satisfied than patients with affective, anxiety, and adjustment disorders. Symptom reduction and changes in the interpersonal domain were important outcomes associated with patient satisfaction. Although pharmacotherapy itself was not related to patient satisfaction, patients who perceived improvements in pharmacotherapy as one of the most important treatment outcomes were less satisfied than others. Preliminary evidence shows that coping with specific problems and symptoms is associated with satisfaction among male patients, whereas changes in the interpersonal domain seem to produce satisfaction among female patients. CONCLUSION: Patient-reported change and diagnostic category appear to play a relevant role in generating patient satisfaction. Further research is needed to clarify the interactions between sex, perceived outcome, and satisfaction.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".