Initiating Cognitive Processing Therapy (CPT) in Community Settings: A Qualitative Investigation of Therapist Decision-Making
Bibliographic record
Abstract
Various organizations have provided treatment guidelines intended to aid therapists in deciding how to treat posttraumatic stress disorder (PTSD). Yet evidence-based psychotherapies (EBPs) for PTSD in the community may be difficult to obtain. Although strides have been made to implement EBPs for PTSD in institutional settings such as the United States Veterans Affairs, community uptake remains low. Factors surrounding clients' decisions to enroll in EBPs have been identified in some settings; however less is known regarding trained therapists' decisions related to offering trauma-focused therapies or alternative treatment options. Thus, the aim of the current study was to examine therapist motivations to initiate CPT in community settings. The present study utilizes data from a larger investigation aiming to support the sustained implementation of Cognitive Processing Therapy (CPT) in community mental health treatment settings. Enrolled therapists participated in phone interviews discussing their opinions of CPT, preferred treatments for PTSD, and process in assessing appropriate PTSD treatments for clients. Semi-structured interviews (N = 29) were transcribed and analyzed using a directed content analysis approach. Several themes emerged regarding therapists' decision-making in selecting PTSD treatments. Therapist motivations to use EBPs for PTSD, primarily CPT, were identified at the client (e.g., perceived compatibility with client-level characteristics), therapist (e.g., time limitations), and clinic levels (e.g., leadership support). The results provide insight into the complex array of factors that affect sustainability of EBPs for PTSD in community settings and inform future dissemination of EBPs, including training efforts in community settings.
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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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".