Unintentional Injuries Among Children of Parents with Diagnosed Mental Health Conditions in Alberta, Canada
Bibliographic record
Abstract
Background: Unintentional Injuries (UI) are a leading cause of hospitalization among children in Alberta, Canada. A small body of global research has found the risk of UI to be higher among children of parents with mental health and addiction conditions (MHAC) as compared to children with parents without MHAC. However, the scope of research related to this phenomenon is limited and has yet to be conducted in Canada. Objectives: This thesis includes three studies each with different objectives. Study 1 descriptively assessed the different types and causes of UIs among children of parents with mood and anxiety disorders (MAD) as compared to children in the general population of Alberta. Study 2 quantified and compared the risk of UI among children of parents with MAD as compared to children with parents without MAD in Alberta. Study 3 examined whether the risk of UI in children with parents with MAD is greater in the acute period following parental diagnosis and if the risk changes over time. Methods: Study 1 used administrative health data to identify a study population of children with parents with MAD and children in the general Alberta population between the ages of 0-9 who had sustained an incident UI. Descriptive analyses identified the most common types and causes of injuries between the populations. Further analysis identified whether the different frequencies were statistically significant between the two populations. An analysis was also conducted to determine if injury types differed depending on how many parents in the household had MAD. Studies 2 and 3 were retrospective case control studies which used administrative health data to identify cases of infant, pre-school and school-aged children aged 0-9 with a UI and age-sex matched controls without a history of UI. Cases and controls were linked to their parental guardians, and parental MHAC status was identified. In study 2 conditional logistic regression was used to determine the odds ratios (ORs) of the risk of UI given parental status of MAD compared to the risk of UI given parental status of no MAD. Unadjusted and adjusted ORs were calculated and reported with 95% confidence intervals (CI). In study 3 the time from parental diagnosis of MAD to the time of UI was calculated and categorized as being within 0-90 days and more than 90 days. Conditional logistic regression was used to determine ORs for the risk of UI given parental status of MAD compared to the risk of UI given parental status of no MAD. ORs were calculated separately for both time periods. Results: Study 1 found children of parents with MAD to be prone to different types of injuries that are more severe and costly in nature. The frequency of injury types among children with parents with MAD was found to be similar regardless of the number of parents with MAD in the household. The causes of injuries were also found to be similar across children with parents with MAD and children in the general population. Study 2 found the crude, unadjusted risk of UI to be elevated among children whose parents had been diagnosed with anxiety disorders (infant OR=1.24; preschool OR=1.25; school-age OR=1.18) and mood disorders (infant OR=1.32; preschool OR=1.15; school-age OR=1.22) compared to children with parents with no MAD. The ORs remained positive, even following propensity score adjustment and adjustment for presence of other MHACs. Study 3 found the risk of UI among children with parents with anxiety and mood disorders was highest during the 0-90 -day period (OR = 1.23; OR=1.40), and decreased but remained positive following the 90-day period (OR=1.18; OR = 1.20). Conclusion: Consistent with global research, children in Alberta with parents with MAD face a higher risk of UI compared to children with parents without MAD. However, a novel finding of this study is that not only is this risk of UI higher during the acute period following parental diagnosis, but children in this population continue to face an elevated risk of UI in the long-term compared to children whose parents do not have a history of MAD. In conclusion, while interventions to support families of parents with MAD are important during the acute period following parental diagnosis, they should also be available long-term.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".