Treatment-related decisional conflict in patients with depressive and anxious disorders
Bibliographic record
Abstract
PURPOSE: To determine the level of treatment-related decisional conflict in patients with emotional disorders and to establish its relationship with sociodemographic and clinical variables. METHODS: We conducted a cross-sectional survey on a convenience sample of 321 consecutive psychiatric outpatients with emotional disorders. All patients completed self-report questionnaires assessing sociodemographic and clinical variables, patients' preference of participation in decision making, perceived decisional conflict about treatment, adherence to prescribed treatment, and satisfaction with the psychiatric care provided. Multiple correspondences analysis was used to investigate relationships of decisional conflict with the variables of interest. RESULTS: Approximately, two-thirds of psychiatric outpatients self-reported decisional conflict regarding their treatment. Interestingly, the presence of decisional conflict did not influence significantly patients' preferences of participation or their adherence to prescribed treatment. Patients without decisional conflict registered significantly higher satisfaction. Multiple correspondences analysis evidenced two clear profiles: patients without decisional conflict received the treatment they preferred, mainly psychotherapy or combined treatment, had been under psychiatric treatment for longer than 5 years, and self-reported high satisfaction with health care received; on the other hand, patients with decisional conflict did not receive the treatment they preferred, were treated with pharmacotherapy alone for a period of time between 1 and 5 years, and self-reported medium satisfaction with received health care. CONCLUSION: The high level of decisional conflict found in patients with depression and anxiety attending a secondary care service could be an important driving force when personalizing and tailoring information and teaching skills to patients about their illnesses and their treatments.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".