The impact of military life on the well-being of children in single-parent military families
Bibliographic record
Abstract
Introduction: The military lifestyle presents unique challenges to children from military families, such as frequent family separations due to operational deployments and training. There is little evidence on how children in single-parent military families adjust to the demands of military life. The current study examined the impact of military life on the well-being and quality of child–parent relationships in single-parent Canadian Armed Forces families. Methods: Focus groups were conducted with 65 single parents from several locations in Canada. Parents were asked about their satisfaction with the quality of the child–parent relationship, their child's well-being, and the phases of deployment presenting the most challenges to their child. Results: Most parents reported that their children were doing well; however, deployment was identified as a major stressor that took a toll on children's well-being. Moreover, for some families, deployment reduced the quality of the child–parent relationship. Discussion: The findings are discussed by comparing the similarities and differences in child well-being and the child–parent relationship within single-parent military families to those within single-parent civilian families and dual-parent military families.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".