Enterprise healthcare physician services in Canada: an environmental scan
Bibliographic record
Abstract
Abstract Background Employers in Canada are increasingly offering physician services to their employees, often through third party workplace “enterprise healthcare” platforms. To date however, little work has been done to understand this method of organizing and delivering care. Objective To understand the nature, extent and implications of enterprise healthcare physician services in Canada. Methods We conducted structured internet and database searches to identify enterprise healthcare platforms that provided physician services and their public websites. To answer our research question, We extracted data from company websites and linked company documents as well as information from Mergent Intellect, a web-based application with business data on Canadian companies. Results We identified nine companies offering enterprise physician services to employees in Canada via 11 enterprise software platforms. According to company claims, over four million Canadian employees and their family members have access to enterprise physician services. All platforms offer virtual physician services and five also facilitate in person visits. Ten of the platforms provide primary care services and one offers only addiction medicine services. Four of the platforms offer to communicate and share information with an employee’s regular primary care provider. Five state they share aggregate or de-identified health data with employers. Conclusions Enterprise healthcare companies provide millions of Canadian employees and their families with rapid access to virtual physician services and, in some cases, in person care. These services may disrupt continuity of care (care by the same provider over time) and pose risks to employee privacy. As other Canadians do not have access to these services, enterprise healthcare is also introducing two-tiered healthcare across Canada potentially affecting the sustainability of the public healthcare system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.052 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".