An institutional ethnography of political and legislative factors shaping online sexual health service implementation in Ontario, Canada
Bibliographic record
Abstract
Public health scholarship is increasingly attuned to the structural determinants of health, such as the associations between macro-level policy and population health outcomes. Yet the ways public healthcare services are specifically made available through political and legislative decisions remain relatively under-explored. Using the critical research strategy of institutional ethnography, this study charts how political leadership transitions and legislative processes animate local public health service implementation activities. We investigated the feasibility of introducing an online sexually transmitted and blood-borne infections (STBBI) testing service to improve sexual healthcare access for gay, bisexual, queer, and other men who have sex with men in Ontario, Canada. Data were collected between June 2019 and June 2020. We conducted interviews with healthcare providers, sexual health program developers and managers, and other public health professionals with expertise in STBBI testing (n = 23), stakeholder meeting observations, and analyses of key texts (e.g. provincial policies and legislation). We uncovered that interpretations of provincial legislation posed a barrier to the online STBBI testing model, and we explicated the work of gaining decision-maker support for this new service during a period of austerity. In response to the election of political leadership who de-funded local public health, participants strategically framed arguments in favour of online testing using discourses of evidence, equity, and cost savings. Our article provides an empirical case study of the mechanisms by which political and legal dimensions direct the implementation of health services, shaping population health outcomes, and health equity in turn.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".