Military Family-Centred Resilience-Building Programming Across the Deployment Cycle: A Scoping Review
Bibliographic record
Abstract
Background: There is international agreement that military families (MFs)—active service members, reservists, veterans, and their families—must be resilient to overcome military life adversities. Resilience is defined either as skillsets or as processes implicating multi-systems in a socio-ecological context. While research on resilience-building specific to children and families who face adversity is growing, there is a paucity of evidence on MF-centred resilience-building. Objective: This review describes the evidence on such resilience-building programming and determines if adversity is considered a barrier or facilitator to resilience-building. Methods: This scoping review yielded 4050 peer-reviewed articles from database inception until December 2023, found in 12 databases. Articles were deduplicated, leaving 1317 that were independently screened for eligibility by two reviewers. Disagreements were resolved through discussion with a third reviewer. Findings: Of these articles, 27 were included; 5 additional articles were also included from other sources. The vast majority of included studies (91%) were conducted in the United States. These 32 articles were organised into categories, including demographics, research methodologies used, resilience program descriptors, and outcomes. Conclusions: Our results reveal that programs on building MF resilience vary widely, often measuring non-resilience health and social outcomes. We provide preliminary insights for MF health and policy. Our review findings will be invaluable for further evidence-based programming that builds resilience in MFs.
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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 | 0.017 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".