Are we family? A scoping review of how military families are defined in mental health and substance use research
Bibliographic record
Abstract
Introduction: While some families may experience poor mental health, substance use, and poor school performance due to service life, the usefulness and applicability of these research findings may be affected by how representative study participants are of the broader population. This article aims to examine how research on mental health and substance use defines a “military family” to understand if the current body of evidence reflects the increasing diversity of this population. Methods: A systematic search of academic articles was conducted in Ovid MEDLINE, Ovid Embase, Ovid PsycINFO, Ebsco CINAHL and ProQuest PILOTS using database-specific subject headings and keyword searches for ‘military’, ‘family’, ‘mental health’ and ‘substance use’. Sociodemographic and military characteristics of study participants were extracted to identify who was and was not included. Results: The most commonly represented family structure was the traditional, heteronormative family comprised of a male service member married to a female civilian with whom they have children. Military couples without children, dual-serving couples, families of LGBTQ personnel, unmarried and new relationships, single parents, male spouses/partners, Veterans not seeking Veterans Affairs (VA) services, and families with additional challenges were regularly not reflected in the research due to implicit or explicit exclusion from studies. Discussion: Research on mental health and substance use among the family members of service personnel continues to reflect the traditional, heteronormative family. Future studies should consider more inclusive definitions of family and creative approaches to recruitment to ensure research in this area reflects the experiences, needs, and strengths of an increasingly diverse military community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.036 | 0.035 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| 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".