Predictors of strain for Canadian caregivers seeking service navigation for their youth with mental health and/or addictions issues
Bibliographic record
Abstract
Caring for youth with mental health and/or addictions (MHA) concerns is associated with caregiver strain, which may lead to negative consequences for youth and their caregivers. These consequences may be mitigated by caregivers and/or youth receiving assistance in navigating the healthcare system. Understanding what factors are associated with caregiver strain may be important in developing and implementing navigation services for such families; nonetheless, limited evidence currently exists regarding the predictors of strain in caregivers seeking navigation support. This study aimed to determine whether (a) the mental health profile of youth and (b) the home and family situation for youth with MHA concerns contribute significantly to strain in caregivers engaged in navigation. Data were collected from 66 adults caring for at least one youth with MHA issues accessing navigation service in Toronto, Ontario, between March and August 2018. Multiple linear regressions were conducted to determine which factors were associated with caregiver strain. The first regression model exploring youth-specific independent variables (adjusted r2 = .478, F6,47 = 9.086, p < .001) demonstrated that lower levels of caregiver-rated youth health (β = −0.577, p = .001) and higher levels of youth mental health symptom severity (β = 0.077, p < .001) significantly predicted higher levels of strain. The second regression model (adjusted r2 = .348, F5,54 = 7.287, p < .001) showed that lower levels of family functioning (β = −0.089, p < .001) significantly predicted higher levels of strain. Higher levels of caregiver strain in caregivers of youth with MHA concerns who are accessing navigation services are associated with lower levels of caregiver-rated youth health, higher levels of youth mental health symptom severity, and lower levels of family functioning. These predictors may be potential targets for providers aiming to reduce caregiver strain, as part of navigation or other healthcare services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".