Video-conferencing Telehealth Linkage attempts to Schools to Facilitate Mental Health Consultation.
Bibliographic record
Abstract
OBJECTIVE: Telehealth to schools may be a strategic approach to expand child mental health service delivery, however, there are only a few published examples. This report describes video-conferencing telehealth linkage attempts to schools to facilitate mental health consultation. METHODS: A series of synchronous video-conferencing linkage strategies were attempted to connect a mental health consultation service to multiple schools in a Canadian setting. Consultation to support the implementation of the Daily Report Card, for students with attentional and behavioural problems, was the core content of this pilot linkage attempt. RESULTS: Synchronous video conference consultations were successfully delivered to six elementary schools across three school districts. Two of three linkage strategies were functional. One used existing health centre-based telehealth units to connect to school-based dedicated tablets with a video collaboration app and reliance on existing school Wi-Fi. A second used existing laptops in both the health and school system linked through a communication platform. A third connection, using 3G/4G hotspots to obviate the need to access school Wi-Fi, was deemed too expensive in this setting. CONCLUSION: The potential to use existing computer hardware to connect mental health providers and schools could facilitate scale-up. However, it is unknown whether mental health systems and school sectors will invest in such linkages and reorganize core mental health services to be delivered in this way.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".