Multilevel factors in providers’ decisions to utilize CPT in military- and veteran-serving treatment settings.
Bibliographic record
Abstract
= 55) participated in interviews regarding their opinions of CPT, preferred treatments for PTSD, and their process in assessing appropriate PTSD treatments for each patient. A directed content analysis approach was used to identify themes for providers' decision-making to utilize CPT within the context of four Consolidated Framework for Implementation Research (CFIR) domains. In the outer setting domain, providers reported disconnect from policy and leadership as a barrier, and in the inner setting CFIR domain, providers reported multiple facilitators: available resources, leadership support, and compatibility with CPT. The CFIR domain for characteristics of the individuals aligned with a theme of theoretical orientation and training as a facilitator. The intervention characteristics domain aligned with facilitators and barriers; complexity of CPT was a barrier, but relative advantage and perceived strength of evidence were facilitators toward implementation. The systems surrounding and supporting EBP delivery within the U.S. VA, Canada OSI, and Canadian Forces clinics have more similarities than differences regarding barriers and facilitators to delivering CPT. Despite variability in funding and training, provider experiences across all three systems suggest similar themes. Further investigation is needed to determine whether these findings extend to community samples or sites not yet offering EBPs. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".