Positive functioning and emotional well-being among military personnel and the general population with and without a history of child abuse in Canada
Bibliographic record
Abstract
Introduction: Child abuse exposure is highly prevalent among military personnel compared with the general population. However, little is known regarding its relationship to compromised positive mental health outcomes, particularly in the Canadian Armed Forces (CAF). The objectives of this study were (1) to describe the prevalence of overall positive functioning and emotional well-being among CAF personnel with and without a child abuse history compared with the Canadian general population (CGP) and (2) to examine the relationships between child abuse and positive functioning and emotional well-being among the CAF compared with the CGP. Methods: Data were drawn from two nationally representative datasets: the 2013 Canadian Forces Mental Health Survey (Regular Forces, n = 6,692, response rate = 79.8%; Reserve Forces, n = 1,469, response rate = 78.7%) and the 2012 Canadian Community Health Survey–Mental Health ( n = 23,395; response rate = 68.9%). Keyes’ Mental Health Continuum–Short Form was used to measure positive functioning and emotional well-being. Results: Compared with the CGP, CAF personnel had reduced functioning and emotional well-being. All child abuse types were associated with increased odds of experiencing moderate and languishing mental health and decreased odds of reporting individual indicators of positive functioning and emotional well-being among the CGP and CAF personnel. One significant population interaction effect was found, indicating that among the CAF, exposure to intimate partner violence had a stronger relationship with moderate mental health than among the CGP. Discussion: Child abuse history may be an important factor to consider when trying to improve positive functioning and emotional well-being among CAF personnel.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".