Refining processing of positive memories technique: client perspectives on components, format, and feasibility
Bibliographic record
Abstract
Background: The Processing of Positive Memories Technique (PPMT) is a novel intervention designed to reduce posttraumatic stress disorder (PTSD) symptoms by enhancing the retrieval and processing of positive autobiographical memories (AM). While initial findings of pilot PPMT studies are promising, further refinement of PPMT is needed to optimize its effectiveness and applicability.Objective: To support this goal, the current study gathered feedback from treatment recipients on the components, format, and feasibility of PPMT with the aim to identify potential areas of modification.Method: Seventy-five trauma survivors (Mage = 30.45 years; 74.7% women) completing all five PPMT sessions provided quantitative and qualitative feedback on PPMT.Results: Quantitative data analyses indicated that most participants were satisfied with the PPMT protocol (e.g. positive AM definition, number of elicited positive AMs and PPMT sessions, time devoted to processing positive AMs, preference for individual sessions); and found PPMT to be feasible (e.g. acceptable, easy to learn, good treatment adherence, comprehension, and retention). Qualitative data further highlighted specific benefits, barriers, areas of modification, and implementation preferences for PPMT. Suggestions to refine PPMT included reducing the frequency of listening to the positive AM audio-recordings, clarifying the rationale of how PPMT addresses posttraumatic symptoms, integrating PPMT with trauma-focused interventions, focusing on positive AMs of events unrelated to the trauma and those occurring post-trauma, providing a rationale for and practice in using the present tense in narratives, incorporating emotion regulation skills, being more flexible in the number of sessions, and providing practice in recalling positive AMs.Conclusion: Overall, PPMT was perceived as a promising therapeutic intervention for trauma survivors, with good feasibility indicators.Trial registration: ClinicalTrials.gov identifier: NCT06680193.
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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 | 0.033 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".