‘Removed from humanity’: a qualitative analysis of attitudes toward abortion providers in anti-abortion individuals in Canada
Bibliographic record
Abstract
OBJECTIVE: The present study explores the content of abortion provider stigma. BACKGROUND: Abortion stigma extends beyond women who have abortions to abortion providers. Previous analyses of anti-abortion bills and rhetoric have revealed stereotypes of abortion providers as dangerous and less trustworthy than other health professionals. METHODS: We present a thematic analysis of one-on-one interviews about attitudes toward abortion providers with Canadian individuals (N = 21) holding an anti-abortion stance. RESULTS: We found participants held two kinds of beliefs about abortion providers: (1) providers are agentic and intentional actors and (2) providers are non-agentic victims of a larger system. While the former subtype of provider was viewed with hostility and disgust, the latter was viewed with pity, with participants suggesting that restriction of abortion would be beneficial for provider well-being. CONCLUSION: We document a new component of abortion provider stigma: the belief that abortion providers are harmed by abortion and that they are to be pitied for this. This 'abortion harms providers' attitude parallels recent anti-abortion arguments that abortion harms women. These stigmatising attitudes both construct the provider as untrustworthy and unable to properly care for women.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".