Examining patterns of dose response for clients who do and do not complete cognitive processing therapy
Bibliographic record
Abstract
Trauma-focused therapies, including Cognitive Processing Therapy (CPT; Resick et al., 2016), are effective at reducing clients' PTSD symptoms. A limitation to these treatments, however, is client completion of them. The current study examined temporal patterns of treatment non-completion and the relationships among non-completion, PTSD, and overall mental health functioning outcomes, among clients in a randomized controlled CPT implementation trial. Two models of symptom change were tested: 1) dose-effect model (i.e., clients uniformly improve with additional sessions at a negatively accelerating rate); and 2) the good-enough level model (i.e., clients remain in therapy until they have achieved sufficient improvement, thus clients who attend fewer sessions improve at quicker rates). Results indicated that 42% of clients did not complete treatment, with most discontinuing between sessions two and five. Data did not fit the dose-effect or good-enough level model. Rather, clients who improved at a greater rate in their PTSD symptoms and overall mental health functioning attended more sessions. The average client had the best outcomes when they completed all 12 sessions. Identifying clients who may be at risk for discontinuing treatment, and making a concerted effort toward retaining them, is imperative to reduce non-completion rates and ultimately improve client outcomes.
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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 | 0.025 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".