Screening questions to identify Canadian Veterans
Bibliographic record
Abstract
Introduction: In Canada, there are an estimated 700,000 Veterans of the Canadian military. Veterans are disproportionately prevalent in sub-populations of males, persons with chronic physical conditions, chronic pain, mental health conditions, and those with disabling activity limitations. Veterans are a population of interest to Canadian researchers, but there is no publicly available comprehensive list of Veterans in Canada. This creates a need for a standard set of screening questions suitable for self-report surveys. This article proposes a series of screening questions to identify Canadian Veterans. Methods: The content of the questions were developed considering self-identity, past Canadian surveys, legislation, and relevant characteristics of Canadian military service. Results: The recommended Canadian Veteran identifier questions are: “Have you ever had any Canadian military service? Was this service with the Regular Force? Reserve Force? Navy? Army? Air Force? Are you currently in the Canadian Armed Forces? What year were you released from the Canadian Armed Forces? What year did you join the Canadian Armed Forces?” Discussion: The consistent use of these screening questions allows for comparisons with other studies and will contribute to a better understanding of Veterans in Canada and of the transition from military to civilian life.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".