Lessons Learned in a Physician Referral to Pediatric Telemental Health Services Program.
Bibliographic record
Abstract
BACKGROUND: This study explores the physician referral and engagement process of a pediatric telemental health program based in a large urban teaching children's hospital, and identifies the processes, strengths and challenges from the perspectives of Primary Care Physicians (PCPs) and telepsychiatrist consultants. METHODS: A mixed methods approach was used. This included an online survey completed by 43 PCPs in Ontario rural communities who had referred patients to the telemental health program. Qualitative interviews were conducted with 11 child/adolescent telepsychiatrists who provide consultations via teleconferencing. RESULTS: The majority of PCPs (61%) reported somewhat to moderate satisfaction with referral experiences. Challenges identified by physicians were related to communication and administration issues including: lack of timely follow-up appointments and continuity of care; lengthy referral forms; and recommendations for mental health services not accessible in their communities. Similarly, psychiatrist consultants expressed frustration with the sparse information they received from referring physicians and most significantly, the absence of appropriate service providers/professionals during the consultation to provide collateral information and ensure uptake of recommendations. CONCLUSION: Telemental health programs provide a valuable service to PCPs and their child and youth clients that could be significantly enhanced with a different consultation model. Such models of service delivery require protocols to educate PCPs, improve communication and information sharing and establish clear expectations between PCPs and telepsychiatry consultants.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".