Telepsychiatry Consultation for Primary Care Treatment of Children and Adolescents Receiving Child Protective Services in Chile: Mixed Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Children and adolescents living under the supervision of child protective services have complex mental health care needs. The scarcity and uneven distribution of specialized mental health teams in Chile may limit the provision and quality of care for this vulnerable population. Telepsychiatry can address such health inequities. OBJECTIVE: The objective of this study was to evaluate the feasibility of a telepsychiatry consultation program for primary health care (PHC) treatment of children and adolescents living under the supervision of child protective services. METHODS: We developed a telepsychiatry consultation program for two rural PHC clinics located in central Chile (Valparaíso Region) and evaluated its implementation using a mixed methods study design. The program consisted of videoconferencing mental health consultation sessions scheduled twice per month (each 90 minutes long), over a 6-month period, delivered by child and adolescent psychiatrists based in Santiago, Chile. We described the number of mental health consultation sessions, participant characteristics, perceived usefulness and acceptability, and experiences with the telepsychiatry consultation program. RESULTS: During the 6-month study period, 15 videoconferencing mental health consultation sessions were held. The telepsychiatry consultation program assisted PHC clinicians in assigning the most adequate diagnoses and making treatment decisions on pharmacotherapy and/or psychotherapy of 11 minors with complex care needs. The intervention was perceived to be useful by PHC clinicians for improving the resolution capacity in the treatments of this patient population. Limitations such as connectivity issues were resolved in most sessions. CONCLUSIONS: The telepsychiatry consultation program was feasible and potentially useful to support PHC clinicians in the management of institutionalized children and adolescents with complex psychosocial care needs living in a poorly resourced setting. A larger scale trial should assess clinical outcomes in the patient population. Regulations and resources for this service model are needed to facilitate sustainability and large-scale implementation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".