The Sequence of Recovery in Long-Term Dynamic Psychotherapy
Bibliographic record
Abstract
Psychotherapy has generally been found both efficacious and effective in producing improvement, but the sequence of recovery across different measures is less studied. A total of 53 patients with mood, anxiety, and/or personality disorders were enrolled in an effectiveness study of long-term dynamic psychotherapy of whom 49 gave some follow-up data. Follow-up interviews over a median of 5 years systematically rated 16 measures of Axis I disorders, symptoms, and functioning. Linear models estimated the rate and amount of improvement and time to recovery for each subject initially not well on each measure. The proportion recovering on each measure varied from 0% to 66.7% with self-destructive symptoms and working half-time leading, followed by self-reported measures of distress and defensive functioning, working full-time and satisfaction, then by observer-rated depression and anxiety, attaining a global assessment of functioning of 61, Axis I disorders, and social role functioning. Participants recovering on satisfaction had higher net proportions of other measures attaining recovery (46.9% versus 2.5%, p < 0.0001) compared with those not satisfied. Sustained recovery unfolds sequentially across a variety of symptoms and functioning measures and is associated with patient satisfaction.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".