System-wide implementation of telehealth to support military Veterans and their families in response to COVID-19: A paradigm shift
Bibliographic record
Abstract
The need for the expansion of telehealth services in behavioural health care existed long before the COVID-19 pandemic. Yet, for a variety of reasons – including technological costs, reluctance of behavioural care providers to adapt telehealth to their practices, privacy concerns, and client aversion to receiving care remotely, among many others– telehealth has not been widely implemented. However, the COVID-19 crisis, and the accompanying social isolation that ensued, necessitated either a swift transition to telehealth delivery of behavioural health care, the termination of behavioural health care, or the clinician continuing to meet face-to-face with clients, placing both the clinician and the client at increased risk of infection. Shifting behavioural health care to a telehealth platform seemed the most sensible and, quite candidly, the only option, although many clinics still operate employing the face-to-face modality. In this article, we describe how an emerging national behavioural health care network, Cohen Veterans Network (CVN) in the United States, rapidly and relatively seamlessly transitioned to a full-service, virtual network of outpatient behavioural health clinics when faced with a national crisis.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".