Treating children's mental health problems. Collaborative solutions for family physicians.
Bibliographic record
Abstract
Most children who have been abused or who have suffered psychological trauma do not receive mental health care. Th ose children who do receive these services, usually receive them from their family practitioners. Th e inadequate number of professionals trained to provide mental health care to children and the lack of attention paid to mental health care in training programs and policy development means family physicians must provide treatment in an area in which they report feeling uncomfortable and unskilled. Collaborative care is an eff ective solution for family physicians treating problems outside their area of expertise. For example, treating children who have been sexually abused is complicated and might require specialized approaches to care. Assessing the effect of specific events on children’s mental health can require direct observation of both children and families to compensate for children’s inability to express problems using adult terminology. Collaborative arrangements where family physicians work directly with child psychiatrists, psychologists, social workers, or other specialized health professionals can lessen the face-to-face time specialists need to spend on this treatment. The familiarity family physicians have with their patients can be used to advantage in these situations. Collaborative mental health care is happening in Canada in a variety of ways, each providing a wealth of resources for family physicians handling children’s complex mental health issues.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.087 | 0.012 |
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".