Pay to skip the line: The political economy of digital testing services for HIV and other sexually transmitted infections
Bibliographic record
Abstract
The value of digital healthcare has been lauded in Canada at local, provincial, and national levels. Digital medicine is purported to enhance patient access to care while promising cost savings. Using institutional ethnography, we examined the potential for publicly funded digital testing for HIV and other sexually transmitted infections (STI) in Ontario, Canada. Our analyses draw from 23 stakeholder interviews with healthcare professionals conducted between 2019 and 2020, and textual analyses of government documents and private, for-profit digital healthcare websites. We uncovered a "two-tiered" system whereby private digital STI testing services enable people with economic resources to "pay to skip the line" queuing at public clinics and proceed directly to provide samples for diagnostics at local private medical labs. In Ontario, private lab corporations compete for fee-for-service contracts with government, which in turn organises opportunities for market growth when more patient samples are collected vis-à-vis digital testing. However, we also found that some infectious disease specimens (e.g., HIV) are re-routed for analysis at government public health laboratories, who may be unable to manage the increase in testing volume associated with digital STI testing due to state budget constraints. Our findings on public-private laboratory funding disparities thus discredit the claims that digital healthcare necessarily generates cost savings, or that it enhances patients' access to care. We conclude that divergent state funding relations together with the creeping privatisation of healthcare within this "universal" system coordinate the conditions through which private corporations capitalise from digital STI testing, compounding patient access inequities. We also stress that our findings bring forth large scale implications given the context of the global COVID-19 pandemic, the rapid diffusion of digital healthcare, together with significant novel coronavirus testing activities initiated by private industry.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".