Predictors of Treatment Duration and Retention in a Study of Long-Term Dynamic Psychotherapy: Childhood Adversity, Adult Personality, and Diagnosis
Bibliographic record
Abstract
Systematic knowledge about patient characteristics that predict the duration of long-term psychotherapy is largely absent. We examined predictors of attrition, retention, and duration of long-term dynamic psychotherapy to delineate the naturalistic history of psychotherapy, specifically focusing on childhood emotional neglect and abuse, adult diagnosis, personality, and functioning as predictors. Fifty-three adults with depressive, anxiety, and/or personality disorders (PDs) were offered at least 3 years of long-term dynamic psychotherapy. The median duration of therapy for the study group was 110 sessions (95% confidence interval [CI] 52-141). Nondynamic characteristics (including demographics; most Axis I diagnoses; depression, anxiety, and distress scores; social and global functioning; and the five personality factors) did not predict number of sessions. Dysthymic disorder, presence of any PD (particularly dependent PD [DPD]), emotional neglect in childhood, and higher adaptive defense style scores predicted a greater number of sessions, while obsessive-compulsive PD (OCPD) predicted fewer sessions. Emotional neglect, DPD, higher adaptive defenses, and OCPD were each unique predictors of duration. A session frequency less than 0.7 sessions per week (36 sessions per year) was associated with a three-fold higher risk for intrinsic attrition (relative risk = 3.04, 95% CI 1.10-8.44). Childhood emotional neglect as well as adult dependency may predispose patients to remain in therapy for longer durations, while some adaptive defenses may allow patients to contain the distressing affects that might otherwise lead to early termination. It remains to be seen whether longer durations of treatments are associated with respectively greater improvement, other things being equal, and whether these findings generalize to other types of treatment.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".