A scoping review of military and Veteran families within international suicidality and suicide prevention research
Bibliographic record
Abstract
The impacts of suicidality on families are well known, which is particularly relevant in at-risk populations, such as active duty military personnel and Veteran communities. This scoping review describes how military and Veteran families have been conceptualized within suicide prevention research. A systematic, multi-database search was conducted, and 4,835 studies were screened. All included studies underwent quality assessment. Bibliographic, participant, methodological, and family-relevant data was extracted and descriptively analyzed into Factors, Actors, and Impacts. In total, 51 studies (2007 – 2021) were included. Most studies focused on suicidality rather than suicide prevention. Factor studies described family constructs as a suicidality risk or protective factor for military personnel or Veterans. Actor studies described families’ roles or responsibilities to act in relation to the suicidality of military personnel or Veterans. Impacts studies described the impacts of suicidality on military and Veteran family members. The search was limited to English language studies. There were few studies on suicide prevention interventions for or including military and Veteran family members. Family was typically considered peripheral to the military personnel or Veteran experiencing suicidality. However, there was also emerging evidence of suicidality and its consequences in military-connected family members.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".