Caring for children and youth from Canada’s military families
Bibliographic record
Abstract
Introduction: The lives of Canada's military families are characterized by persistent stressors that can play a role in the health and development of children and youth. Military families are cared for by civilian physicians who may not be aware of this unique experience and risk. Our study sought to explore the knowledge and experiences of paediatricians providing care to Canadian Armed Forces (CAF) families. Methods: A 14-item survey was disseminated electronically by the Canadian Paediatric Surveillance Program (CPSP) to all 2799 Canadian paediatricians and paediatric specialists registered with the CPSP. Questions were focused on: knowledge of CAF families; the impact of the military on family care; confidence in providing care to CAF families; and training/education needs. Results: A total of 774 (28%) completed surveys were received. Approximately one third of respondents incorrectly believed that CAF families receive services from the federal military healthcare system. Nearly one quarter did not feel that identifying for military status informed patient care. Over half of respondents do not feel adequately prepared to provide care to CAF families. Discussion: Findings from this exploratory study suggest that additional resources and training would benefit the care of CAF families. Conclusion: CAF families experience a collection of risk factors that may negatively affect their health and access to services. The survey findings provide evidence of a need to further military literacy amongst Canadian paediatricians and provide direction for the development of enhanced resources and supports.
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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 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".