Collaborative risk-driven intervention: research supporting technology-enabled opportunities for upstream virtual services in rural and remote communities
Bibliographic record
Abstract
In 2011, Canada’s Hub Model of Collaborative Risk-Driven Intervention was launched in Prince Albert, SK. Since that time, over 60 communities across the country have replicated the initiative, resulting in over 9,500 rapid interventions of acutely-elevated risk. For the most part, however, these multi-sector efforts to detect elevations in risk, share limited information, and mitigate risk before harm occurs, have taken place in small-to-large-size communities. Still uncertain, is how the benefits of the Hub Model can be expanded to support individuals in rural and remote communities. This article represents a compilation of extracts from a larger body of work conducted to research, explore, and propose a pilot project for application of collaborative risk-driven intervention in a virtual environment. Part of this effort includes a review of literature on the Hub Model, adaptations of human service initiatives, and the relationship between human service provision and information and communication technology (ICT). Consultations with 199 different human service and ICT professionals lay the groundwork for development of theory, assumptions, risks, options, and solutions for implementation of a tech-enabled Hub. Of course, the implications for service mobilization through a remote presence extend far beyond just the Hub Model. Therefore, this article aims to encourage and inspire action-based research that propels a wide variety of tech-enabled opportunities for improving community safety and well-being.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".