Supporting Resilience in Military Families – from Research to Practice
Bibliographic record
Abstract
Like many Canadian families, military families deal with struggles around financial stress, intimate partner relationships, mental health, and personal well-being. But military families also face challenges that are altogether unique to the military lifestyle: relocation due to operational postings, repeated absences due to military requirements and taskings, and risk of injury and death. Quite often the combination of these challenges compound each other. Moreover, many of these uniquely military challenges are systemic and repetitive – meaning families will be faced with them again and again. Resilience, the ability to bounce back from adversity, can help families navigate these repetitive and systemic challenges by learning to adapt to changing environments quickly and positively. Military Family Services, a division of Canadian Forces Morale and Welfare Services, relies on research as the grounding for modernization of services to better support the resilience of Canadian military and Veteran 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 | 0.061 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".