Mental Health Care Seeking in the Canadian Armed Forces Post-Afghanistan: Can Social Support and Paraprofessional Initiatives Help Increase Access to Care?
Bibliographic record
Abstract
Recent research shows an increase in the mental health needs of Canadian Armed Forces (CAF) personnel (Fikretoglu, Liu, Zamorski, & Jetly, 2016). Research also indicates that a portion of CAF personnel with a mental health need do not seek professional mental health care or fail to initiate treatment in a timely fashion (e.g., Fikretoglu, Liu, Pedlar, & Brunet, 2010; Zamorski & Boulos, 2014). Andersen’s (1995; 2008) Behavioral Model of Health Services Use suggests predisposing factors (e.g., age), enabling/impeding factors (e.g., income), and need-related factors (e.g., mental health diagnosis) can help explain professional mental health care seeking behaviors. Several studies conducted with military samples have investigated factors that can impede the care seeking process (i.e., barriers; e.g., Sareen, Cox, et al., 2007); however, relatively less is known about factors that may facilitate access to mental health care. The current studies explored recent patterns of professional and paraprofessional mental health service use in CAF personnel. The studies were designed to identify different means by which access to mental health care may be increased in this population. Specifically, Study 1 assessed the propensity of CAF personnel to seek help from their social network (e.g., family, coworkers), as well as the impact of seeking such help on professional mental health service use and perceived need for care. Study 2 examined individual predictors of using a paraprofessional peer support program available to CAF personnel, veterans, and their families (i.e., Operational Stress Injury Social Support [OSISS]). Study 3 identified the frequency of Internet use for mental health related activities among CAF personnel and individual predictors of use. Participants in all three studies included Regular Members from a recent nationally representative Canadian military sample (n ≈ 6,700; Canadian Forces Mental Health Survey; Statistics Canada, 2014). ii Weighting and bootstrapping estimation procedures were used to account for the complex survey design. Prevalence estimates were computed for all three studies and multivariate logistic regression analyses served to identify predictors of professional mental health service use, perceived need for care, OSISS use, and Internet use for mental health related activities. The results indicate that: 1) seeking support from various social groups is positively related to professional mental health service use and perceived need for care; 2) meeting criteria for posttraumatic stress disorder has the strongest association with OSISS use, but only a small number of CAF personnel seek help from OSISS; and 3) the Internet is more readily accessed than other forms of paraprofessional mental health care (e.g., OSISS) and few individual barriers exist to Internet use for mental health related activities among CAF personnel. The results suggest that developing psychoeducational programs and resources readily available to the social networks of military personnel (e.g., family members) may help facilitate access to professional mental health care. The results also suggest mental health resources may be best delivered to military personnel and their social networks through in-person professional mental health services or Internet-based technologies. Comprehensive results, methodological considerations, implications, and future research 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".