Stepped collaborative care for pain and posttraumatic stress disorder after major trauma: a randomized controlled feasibility trial
Bibliographic record
Abstract
PURPOSE: To examine feasibility and acceptability of providing stepped collaborative care case management targeting posttraumatic stress disorder (PTSD) and pain symptoms after major traumatic injury. MATERIALS AND METHODS: = 17) group (46% of eligible patients). The intervention was adapted from existing stepped collaborative care interventions with input from interdisciplinary experts and people with lived experience in trauma and disability. The proactive case management intervention targeted PTSD and pain management for 6-months using motivational interviewing, cognitive behavioral therapy strategies, and collaborative care. Qualitative interviews explored intervention acceptability. RESULTS: Intervention participants received a median of 7 h case manager contact and reported that they valued the supportive and non-judgmental listening, and timely access to effective strategies, resources, and treatments post-injury from the case manager. Participants reported few disadvantages from participation, and positive impacts on symptoms and recovery outcomes consistent with the reduction in PTSD and pain symptoms measured at 1-, 3- and 6-months. CONCLUSIONS: Stepped collaborative care was low-cost, feasible, and acceptable to people at risk of PTSD or pain after major trauma.IMPLICATIONS FOR REHABILITATIONAfter hospitalization for injury, people can experience difficulty accessing timely support to manage posttraumatic stress, pain and other concerns.Stepped case management-based interventions that provide individualized support and collaborative care have reduced posttraumatic stress symptom severity for patients admitted to American trauma centers.We showed that this model of care could be adapted to target pain and mental health in the trauma system in Victoria, Australia.The intervention was low cost, acceptable and highly valued by most participants who perceived that it helped them use strategies to better manage post-traumatic symptoms, and to access clinicians and treatments relevant to their needs.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".