Slow implementation of mifepristone medical termination of pregnancy in Quebec, Canada: a qualitative investigation
Bibliographic record
Abstract
Objectives: Mifepristone for first-trimester medical termination of pregnancy (MTOP) became available in Quebec in 2018, one year after the rest of Canada. Using the theory of the Diffusion of Innovation (DOI) and the transtheoretical model of change (TTM), we investigated factors influencing the implementation of mifepristone MTOP in Quebec.Material and Methods: Semi-structured interviews were conducted with 37 Quebec physicians in early 2018. Deductive thematic analysis guided by the theory of DOI explored facilitators and barriers to physicians’ adoption of mifepristone MTOP. We then classified participants into five stages of mifepristone adoption based on the TTM. Follow-up data collection one year later assessed further adoption.Results: At baseline, three physicians provided mifepristone MTOP (Maintenance) and two were about to start (Action). Thirteen physicians at Preparation and Advanced Contemplation stages intended to start while, within the Slow Contemplation, two intended to start and ten were unsure. Seven had no intention to provide mifepristone MTOP (Pre-Contemplation). Major reported barriers were: complexity of local health care organisations, medical policy restrictions, lack of support, and general uncertainty. One year later, ten physicians provided mifepristone MTOP (including three at baseline) and nine still intended to, while seventeen did not intend to start provision. Seven of sixteen participants (44%) who worked in TOP clinics at baseline were still not providing MTOP with mifepristone one year later.Conclusion: Despite ideological support, mifepristone MTOP uptake in Quebec is slow and laborious, mainly due to restrictive medical policies, vested interests in surgical provision and administrative inertia.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".