DEPLOYMENT STATUS: A DIRECT OR INDIRECT EFFECT ON MOTHER–CHILD ATTACHMENT WITHIN A CANADIAN MILITARY CONTEXT?
Bibliographic record
Abstract
Research has suggested that military spouses experience increased depressive symptoms and parenting stress during a military member's deployment. A relationship between maternal depressive symptoms, parenting stress, and child attachment security has been found in the general population, as has an indication that social support may provide a buffering effect. While there appears to be an association between the emotional well-being of military spouses and child emotional well-being during deployment, data are limited regarding the association between maternal emotional well-being and child attachment security. The current study explores the association between deployment status and child attachment to the nonmilitary parent (i.e., the mother in this study) in a sample of 68 Canadian military families. Results revealed a significant impact of deployment status on maternal depressive symptoms and on quality of child attachment. The impact of deployment status on attachment was not mediated through the maternal variables, and despite a main effect of social support on the maternal variables, there was no moderating effect. Thus, our results suggest that deployment may affect child attachment independently of maternal well-being.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".