Addressing the determinants of child mental health: intersectionality as a guide to primary health care renewal.
Bibliographic record
Abstract
Primary health care (PHC) renewal was designed explicitly to attend to the multidimensional factors impacting on health, including the social determinants of health. These determinants are central considerations in the development of integrated, cross-sectoral, and multi-jurisdictional policies such as those that inform models of shared mental health care for children. However, there are complex theoretical challenges in translating these multidimensional issues into policy. One of these is the rarely discussed interrelationships among the social determinants of health and identities such as race, gender, age, sexuality, and social class within the added confluence of geographic contexts. An intersectionality lens is used to examine the complex interrelationships among the factors affecting child mental health and the associated policy challenges surrounding PHC renewal. The authors argue that an understanding of the intersections of social determinants of health, identity, and geography is pivotal in guiding policy-makers as they address child mental health inequities using a PHC renewal agenda.
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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 | 0.002 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".