Experiences of stigma amongst healthcare professionals working in abortion care: a global survey
Bibliographic record
Abstract
INTRODUCTION: Abortion-related stigma negatively affects healthcare professionals providing abortion care, threatening workforce well-being and service provision. This global study, part of the Royal College of Obstetricians and Gynaecologists' 'Making Abortion Safe' Project, explored healthcare providers' experiences of abortion stigma, its drivers and mitigating factors. METHODS: A global online survey was distributed to healthcare professionals trained to provide abortion and post-abortion care (PAC) through 16 partner organisations over a 6-week period in 2021. The Abortion Provider Stigma Scale (APSS) was adapted, and linear regression modelling was used to examine the relationships between demographic variables, attitudes towards abortion, workplace burnout and total APSS scores. RESULTS: In 1674 providers from 77 countries, stigma was universally experienced. Higher stigma levels were associated with countries with restrictive abortion laws; working in non-governmental organisation settings; and providing first- and second-trimester abortions compared with only PAC. A large majority (84%) of providers reported feeling burnout to some degree, with a strong correlation between APSS scores and workplace burnout. Providers exposed to both values clarification and attitude transformation (VCAT) training and other support workshops reported more positive attitudes and lower stigma compared with those with only VCAT or no training. CONCLUSIONS: Legal reform is needed to reduce stigma for providers as part of broader initiatives on women's reproductive rights in general. Meanwhile, ongoing support at the organisational level, alongside addressing stigmatising values and attitudes, can help create positive workplaces and resilient providers. Mainstreaming and integrating abortion services into public health systems would also help normalise abortion care.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".