Autonomous and controlled motivation and interpersonal therapy for depression: Moderating role of recurrent depression
Bibliographic record
Abstract
OBJECTIVES: We examined the moderating role of depression recurrence on the relation between autonomous and controlled motivation and interpersonal therapy (IPT) treatment outcome. DESIGN: The investigation was conducted in an out-patient mood disorders clinic of a large university-affiliated psychiatric hospital. The sample represents a subset of a larger naturalistic database of patients seen in the clinic. METHODS: We examined 74 depressed out-patients who received 16 sessions of IPT. The Beck Depression Inventory-II, administered at pre-treatment and post-treatment, served as a measure of depressive severity. Measures of motivation and therapeutic alliance were collected at the third session. RESULTS: In the entire sample, both the therapeutic alliance and autonomous motivation predicted higher probability of achieving remission; however, the relation differed for those with highly recurrent depression compared to those with less recurrent depression. For those with highly recurrent depression, the therapeutic alliance predicted remission whereas autonomous motivation had no effect on remission. For those with less recurrent depression, both autonomous motivation and the therapeutic alliance predicted better achieving remission. Controlled motivation emerged as a significant negative predictor of remission across both groups. CONCLUSION: Taken together, these results highlight the possible use of motivation theory to inform and enrich therapeutic conceptualizations and interventions in clinical practice, but also point to the importance of modifying interventions based on the chronicity of a client's depression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".