Autonomous motivation for therapy: A new common factor in brief treatments for depression
Bibliographic record
Abstract
The authors propose a new common treatment factor, autonomous motivation (Deci & Ryan, 2000 Deci, E. L. and Ryan, R. M. 2000. The what and the why of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11: 227–268. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]), defined as the extent to which patients experience participation in treatment as a freely made choice emanating from themselves. Ninety-five depressed outpatients were randomly assigned to receive 16 sessions of manualized interpersonal therapy, cognitive–behavior therapy, or pharmacotherapy with clinical management. Self-report and interviewer-based measures of depressive severity were collected at pretreatment and posttreatment. Autonomous motivation, therapeutic alliance, and perceived therapist autonomy support were assessed at Session 3. Autonomous motivation was a stronger predictor of outcome than therapeutic alliance, predicting higher probability of achieving remission and lower posttreatment depression severity across all three treatments. Patients who perceived their therapists as more autonomy supportive reported higher autonomous motivation.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".