Political Barriers to Abortion Access in New Brunswick: A Qualitative Exploration of a Political Hot Potato
Bibliographic record
Abstract
New Brunswick has been an outlier concerning abortion access for decades. In 2015, the Liberal government under Premier Brian Gallant made significant legislative changes to regulation 84–20 to make abortions more easily accessible in the province. Despite these changes, the Liberal government fell short of publicly funding community-based abortions outside hospitals. This qualitative study aimed to examine the political dynamic and pressures explaining why New Brunswick remains behind on abortion access compared to other Canadian provinces. From September until November 2020, we conducted semi-structured in-depth interviews with 22 participants. Participants were a mix of past and present politicians, health care leaders, and high-ranking public officials identified as key stakeholders in the abortion debate in New Brunswick. Some participants shared their challenges separating their personal views on abortions and admitted wanting to limit access to make it difficult for people to get an abortion. At the same time, other participants described concern for the most vulnerable segments of the New Brunswick population who may struggle with accessing abortions, leading to undue stress and anxiety levels. Ultimately, this article describes the challenges politicians face with power dynamics within their parties and the balance between managing political risks to maintain popularity within the New Brunswick electorate without infringing on people’s right to medical care.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".