A comparative analysis of non-invasive prenatal testing in Ontario and Quebec: the role of governing style in health technology innovation & adoption
Bibliographic record
Abstract
BACKGROUND: While processes of adoption and the impacts of various health technologies have been extensively studied by health services and policy researchers, the influence of policy makers' governing styles on these processes have been largely neglected. Through a comparative analysis of non-invasive prenatal testing (NIPT) in the Canadian provinces of Ontario and Quebec, this article examines how decisions about this technology were shaped by contrasting political ideologies, resulting in vastly different innovation and adoption strategies and outcomes. METHODS: A comparative qualitative investigation comprising of a document analysis followed by semi-structured interviews with key informants. Interview participants were researchers, clinicians, and private sector medical laboratory employees based in Ontario and Quebec, Canada. Interviews were conducted both in person and virtually- owing partly to the COVID-19 pandemic - to garner perspectives regarding the adoption and innovation processes surrounding non-invasive prenatal testing in both provinces. All interviews were recorded and transcribed verbatim and data were analyzed using thematic analysis. RESULTS: Through an analysis of 21 in-depth interview transcripts and key documents, the research team identified three central themes: 1) health officials in each province demonstrated a unique approach to using the existing scholarly literature on NIPT; 2) each provincial government demonstrated its own preference for service delivery, with Ontario preferring private and Quebec preferring public; and finally, 3) both Ontario and Quebec's strategies to NIPT adoption and innovation was contextualized within each province's unique financial positioning and concerns. These findings illustrate how both Quebec's nationalist focus and use of industrial policy and Ontario's 'New Public Management' style had implications for how this emerging healthcare technology was made available within each province's publicly-financed health system. CONCLUSIONS: Our study reveals how these governments' differing approaches to using data and research, public versus private service delivery, and financial goals and concerns resulted in distinct testing technologies, access, and timelines for NIPT adoption. Our analysis demonstrates the need for health policy researchers, policy makers, and others to move beyond analyses solely considering clinical and health economic evidence to understand the impact of political ideologies and governing styles.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".