Towards Technological Health: Improving Access to Public Healthcare in B.C. by Integrating Virtual Care and Predictive AI
Bibliographic record
Abstract
Access to public healthcare within British Columbia is inadequate and unequally distributed. This decreases physical and mental health outcomes for patients and their healthcare workers. B.C.’s five regional health authorities (RHAs) have failed over 50% of all performance targets set annually by the Ministry of Health, which has limited power due to the fragmented provincial system. Virtual care seemed like a prominent solution during the onset of the COVID- 19-induced lockdown, yet support has decreased since in-person activities resumed. However, Fraser Health is actively implementing new virtual care efforts integrated with predictive AI systems. Predictive AI has the potential to increase virtual care serviceability and provide a detailed schema of day-to-day operations, encouraging improvements towards performance targets that ultimately increase access to public healthcare. To achieve this potential, independent panels and analysis of previous new-technology scale-ups within established healthcare systems, such as eConsult, are advised.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".