Caring for children with mental health or developmental and behavioural disorders: Perspectives of family health teams on roles and barriers to care.
Bibliographic record
Abstract
OBJECTIVE: To inform a shared care model between developmental and behavioural (DB) and mental health specialists and primary care physicians by having members of primary care family health teams (FHTs) report on strengths of and barriers to providing care for children with DB disorders and mental health concerns. DESIGN: Qualitative study using semistructured focus groups. SETTING: Academic and community-based FHTs in Toronto, Ont. PARTICIPANTS: Primary care physicians, nurses, allied health professionals, and family medicine trainees within the participating FHTs. METHODS: Nine focus groups were conducted with FHT members, and transcripts were analyzed for key themes using an inductive thematic analysis approach. MAIN FINDINGS: Eighty-four participants across 9 sites were interviewed. Six sites were academically affiliated and 3 were community based. Participants described their roles in the care of children with DB disorders as primarily "referral agent" but also as "long-term supporter" and "health care coordinator." Family health team members expressed the desire to "learn" and "do more" for these children but noted numerous barriers to providing care, captured in 4 overarching themes: limited training beyond how to screen, lack of service knowledge, limited time and communication, and cumbersome access to mental health and dual diagnosis services. CONCLUSION: Primary care physicians are in the unique position of being able to provide longitudinal care for children with DB and mental health disorders. However, they perceive barriers to providing care that can affect access to services, service quality, and health outcomes for these children and their families. The health system might benefit from addressing these barriers by providing more training for primary care physicians in the longitudinal care of children with mental health and DB disorders, and by improving communication between FHTs and DB and mental health specialists regarding service navigation and emerging comorbidities. A shared care model integrating DB and mental health specialists into primary care might be one approach that warrants implementation and research.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".