Access to Internet-based mental health resources in a nationally representative sample of Canadian active duty military personnel
Bibliographic record
Abstract
Introduction: The Canadian Armed Forces (CAF) have made access to mental health care a priority. Access to care is typically conceptualized as in-person interactions with health care providers; however, it can also include virtual health care services. Virtual health care is health services delivered through an Internet platform. Internet-based interventions are promising for increasing mental health care access among CAF personnel; however, increased reliance on Internet technology for service provision may create disparate access. Accordingly, a recent nationally representative sample of CAF Regular Forces personnel was examined with the following aims: (1) provide estimates of different types of Internet use for mental health-related problems and contrast such estimates with usage rates for other forms of professional and paraprofessional care; (2) examine the relationship between Internet use for mental health-related problems, professional mental health service use, and perceived need for care; and (3) identify individual predictors of Internet use for mental health-related problems. Methods: Prevalence estimates were computed for all variables of interest and multivariate logistic regression analyses served to identify predictors of Internet use. Results: The results indicate that the Internet is more readily accessed for mental health care than other forms of paraprofessional services but remains less commonly accessed than in-person mental health care providers. Results also indicate that the Internet is primarily used to obtain information about symptoms or where to get help. Discussion: Findings suggest few individual barriers exist for accessing the Internet and Internet-based technologies may be a viable alternative for increasing access to mental health resources among CAF personnel and their families.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".