Building capacity to care for Veterans and families: Results of a mental health service provider needs assessment
Bibliographic record
Abstract
Introduction: Community mental health service providers are an integral element of support in the Canadian mental health system for Veterans, yet their capacity needs specific to caring for Veterans is not well understood. Methods: A national needs assessment survey was disseminated to mental health service providers who care for Canadian Armed Forces Veterans and retired Royal Canadian Mounted Police members using a multi-pronged strategy. Outreach focused on community providers registered to deliver services to Veterans Affairs Canada clients, to understand capacity needs, including cultural competency needs. An optional sub-survey assessed capacity needs specific to providing services to those impacted by military sexual trauma. Descriptive analysis of these data was conducted. Results: There were 696 people who completed the survey, 669 of whom identified as a service provider. Most respondents (509) agreed to participate in the sub-survey. Of the service provider respondents, 76% were trained in cognitive behavioural therapy. Of the respondents who completed the sub-survey, 12% reported receiving specialized training in caring for someone impacted by military sexual trauma. Discussion: While most respondents had training in at least one of the evidence-based therapies for posttraumatic stress disorder (PTSD), respondents had the least training in prolonged exposure therapy and cognitive processing therapy, despite these being strongly recommended in PTSD treatment guidelines. It is unclear from the survey how closely evidence-based therapies are being delivered with fidelity to their protocols. There is a dearth of training among service providers in sexual trauma, signalling an area for future training development.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".