Prolonged Exposure in the Treatment of PTSD Following an Apartment Fire
Bibliographic record
Abstract
Foa, Hembree, and Rothbaum’s Prolonged Exposure treatment manual has been found to be an effective treatment modality for posttraumatic stress disorder (PTSD) that is comparable with other evidence-based PTSD treatments. Although the manual details weekly 90-min sessions, this time frame is not always feasible for those clinicians who may be confined to a 50-min appointment. In addition, Foa et al.’s inclusion of audio and video for taped exposures is not always possible given some centers’ technological limitations and client finances. The current case summary details an adapted version of the manual that takes into account such limitations to treat a 26-year-old Jamaican-Canadian female who presented for treatment of PTSD following a fire destroying her home and possessions. Eighteen weekly 50- to 60-min sessions included components of psychoeducation, breathing retraining, in vivo exposures, imaginal exposures, and a trauma narrative (in replacement of audio/video exposures). From pre- to postassessment, significant gains were noted, including a reduction in intrusive thoughts and hyperarousal, and elimination of reexperiencing and sleep disturbances. At the conclusion of treatment, the client also demonstrated an ability to confront the trauma, memories, situations, activities, and places that she had avoided. Self-report measures further validated gains. Due to complicating factors, a follow-up evaluation was not able to be completed. Treatment complications, diversity factors, and implications for future work are discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".