Implementing integrated-youth services virtually in British Columbia during the COVID-19 pandemic
Bibliographic record
Abstract
Objective: During the COVID-19 pandemic, Foundry responded to support youth across the province of British Columbia (BC), Canada, by creating a virtual platform to deliver integrated services to youth. In this paper, we report on the development of Foundry Virtual services, initial evaluation results and lessons learnt for others implementing virtual services. Methods and analysis: In April 2020, Foundry launched its virtual services, providing young people and their caregivers from across BC with drop-in counselling services via chat, voice or video calls. Foundry consulted with youth and caregivers to implement, improve and add services. Using Foundry's quality improvement data tool, we document service utilisation, the demographic profile of young people accessing virtual services, and how young people rate the quality of services accessed. Findings: Since launching, 3846 unique youth accessed Foundry Virtual services over 8899 visits, totalling 11 943 services accessed. The predominant services accessed were walk in counselling (32.5%), mental health and substance use services (31.4%), youth peer support (17.2%) and group services (7.3%). Over 95% of youth reported that they would recommend virtual services to a friend. Conclusion: In response to our early findings, we provide three recommendations for other implementers. First, engage the audience in which you intend to serve at every phase of the project. Second, invest in the needs of staff to ensure they are prepared and supported to deliver services. Last, imbed a learning health system to allow for the resources culture of continuous learning improvement that allows for rapid course adjustments and shared learning opportunities.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".