Problematic Prescription Drug Use Among Canadian Armed Forces Veterans: Data from the Canadian Armed Forces Members and Veterans Mental Health Follow-Up Survey (CAFVMHS)
Bibliographic record
Abstract
BACKGROUND: Problematic prescription drug use (PPDU) is a public health concern that can cause degradation of wellbeing. Examination of prevalence and correlates of PPDU in Canadian Armed Forces (CAF) Veterans is essential to understand its impact and to identify those at risk. This study aimed to assess correlates of PPDU among CAF Veterans, including sociodemographic characteristics and physical and mental disorders. METHODS: = 1,922), all of whom indicated Veteran status at the time of the survey. PPDU was defined as utilization of a prescription drug without a prescription, in excess to the amount prescribed, for recreational purposes, or to a level where the individual felt they could not stop usage. PPDU included three categories of substances: sedatives/tranquilizers, stimulants, and analgesics. RESULTS: Nine percent of Veterans indicated PPDU in the past year, while 16.8% endorsed PPDU in their lifetime. Being unpartnered increased the odds of PPDU, while older age, air environment, and officer rank were associated with lower odds. Past-year presence of a mental disorder, alcohol use disorder, suicidal behavior, chronic pain condition, and greater number of physical health conditions demonstrated positive associations with PPDU (Adjusted Odds Ratios [AORs] ranging from 1.36 to 5.31). Increasing number of traumatic events and deployment-related experiences led to greater odds of PPDU (AORs of 1.12 and 1.16, respectively). CONCLUSIONS: Results highlight the vulnerability of the CAF Veteran population to PPDU. Correlates noted may aid in the development of supports to promote the mental health of Veterans.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".