Studying military and Veteran health using a life-course approach: Lessons learned from a Canadian record linkage study
Bibliographic record
Abstract
Introduction: For military personnel, the life course can consist of pre-military, military, transition, and post-military stages. An example of a life-course approach is the record-linkage Canadian Forces Cancer and Mortality Study (CFCAMS). This article provides an overview of CFCAMS challenges and solutions. Methods: Challenges are organized into four categories: institutional, data driven, budget based, and human resource related. Results: Institutional challenges arise from the different mandates of the Department of National Defence and Veterans Affairs Canada. These challenges were addressed by collaboration with Statistics Canada and endorsement of the life-course research questions by all three departments. Data-driven challenges arise from data collected disjointedly for different stages of the life course. These challenges were addressed by data linkage in a secure environment that respected privacy and confidentiality. Budget-based challenges arise from the financial costs of creating new datasets. These challenges were addressed for topics that aligned with funded priorities. Human-resource-related challenges include recruitment and retention of experienced personnel. These challenges are exacerbated by the complexity of the data linkage environment, and addressing them is an ongoing issue. Discussion: Any research agenda that uses a life-course approach could address many policy-relevant research questions, but this ability is constrained by several diverse, yet interconnected, challenges. Stakeholder expectations may be unrealized when the interrelationship between these challenges leaves unfilled gaps. Collaborative research can only be successfully conducted if challenges in all four categories are addressed. The CFCAMS experience provides an understanding of the roles and structures required to generate research results to influence policy and practice.
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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 | 0.213 | 0.322 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".