Modifications to a Collaborative Network During the COVID-19 Pandemic: Adapting to a Changing Landscape to Meet Community Needs
Bibliographic record
Abstract
Individuals living in the United States of America experienced remarkable changes to their activities, routines, and facets of their daily life as a result of the coronavirus or COVID-19. Mitigation strategies, including social distancing, telework and telemental health (TMH), have had significant implications in neighborhoods and communities. Research has indicated community collaboration in behavioral health is a key factor in meeting the health needs of individuals through the organization of resources, shared communication, and an understanding of the roles of different community agencies (Christens & Inzeo, 2015; Walzer, Weaver, & Mcguire, 2016). As a result of COVID-19, Central Virginia’s behavioral healthcare and human services agencies shifted from largely face-to-face contact to a telehealth delivery of care through audio and video conferencing. The purpose of this article is to present a case study on the modifications made by a human services collaborative network in Central Virginia which may provide generalized lessons that other agencies and collaborative networks consider when adapting to address an unforeseen pandemic. Prior to discussing modifications and offering generalized lessons learned, a description of the collaborative network including the guiding theory and how the theoretical framework shaped the modifications will be presented. Keywords: community, collaboration, pandemic, COVID-19, behavioral health, telework, lessons learned
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".