Beyond first-line approaches: A scoping review of emerging operational stress interventions for military and public safety personnel
Bibliographic record
Abstract
Introduction: Mental health conditions associated with military service, such as posttraumatic stress disorder (PTSD), can affect other high-risk public safety personnel (PSP) groups. Traditional psychotherapeutic and pharmaceutical approaches are effective for approximately 50%-60% of this population; thus, a broader range of treatment approaches needs to be identified. This scoping review aimed to identify and summarize programs available in Canada. Methods: Several databases were searched between 2011 and 2022, using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews to identify mental health programs for those at risk of developing occupation-based posttraumatic stress. Grey literature and a public web-based database were also searched. Results: After screening 599 records, 13 primary literature studies were identified. An additional seven programs were found by assessing 126 programs and services found in a public database search. Programs and services included a variety of prevention and intervention approaches, many of which used at least one complementary or alternative medicine modality. Discussion: Numerous therapeutic programs are available in Canada for military and PSP populations, many of which include a novel and holistic approach to treating mental health issues. However, there are few outcome studies, which may translate to the reluctance of some service providers to use or recommend these programs for mental health interventions or rehabilitation. More research is needed in these areas to better understand the utility of integrated approaches, which may help individualize and optimize mental health treatment in military and PSP populations.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".