Child Abuse Experiences and Perceived Need for Care and Mental Health Service Use among Members of the Canadian Armed Forces
Bibliographic record
Abstract
OBJECTIVE: Child abuse is associated with poor mental health outcomes in adulthood. However, little is known about how a history of child abuse may be related to perceived need for care (PNC) and mental health service use (MHSU) among Canadian military personnel. The objectives of this study were to determine 1) the relationship between child abuse history and PNC and 2) the relationship between child abuse history and MHSU in the Canadian military. METHOD: Data were drawn from the 2013 Canadian Forces Mental Health Survey ( n = 6692 Regular Force personnel between the ages of 18 and 60 years). Logistic regression was used to examine the relationships between individual child abuse types and PNC and MHSU while adjusting for sociodemographic variables, the presence of mental disorders, deployment-related variables, and other types of child abuse. Population attributable fractions (PAFs) were calculated to estimate the proportion of PNC and MHSU that may be attributable to child abuse. RESULTS: Each individual child abuse type was associated with increased odds of PNC and MHSU after adjusting for all covariates (adjusted odds ratio ranging from 1.26 to 1.80). PAFs showed that if any child abuse did not occur, PNC and MHSU among Regular Force personnel may be reduced by approximately 14.3% and 11.3%, respectively. CONCLUSIONS: This study highlights that preenlistment factors, such as a history of child abuse, have an independent association with PNC and MHSU and hence need to be considered when assessing the mental health service needs of the Canadian Regular Force 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".