User-Centered Design of a Health Professional Mentorship Platform to Improve Equitable Access to Abortion (Preprint)
Bibliographic record
Abstract
BACKGROUND Access to safe abortion care is a reproductive right for all individuals across Canada. Underserved populations are over-represented among those with unintended pregnancies and particularly those seeking abortion. Yet, few resources exist to help healthcare and allied helping professionals provide culturally competent, and gender-affirming abortion care for underserved populations. OBJECTIVE This project aimed to redesign and adapt an existing subscription-based medication abortion mentorship platform into a culturally appropriate and gender-affirming open-access website of curated health professional resources to promote equitable, accessible, high-quality abortion care, particularly for underserved populations METHODS We drew on a user-centered design framework to redesign the web platform in five iterative phases. Healthcare and allied helping professionals were engaged in each stage of the development process including the initial design of the platform, curation of the resources, reviewing the content, and evaluation of the wireframes and the end product. RESULTS This project resulted in an open-access bilingual (English and French) online platform containing comprehensive information and resources on abortion care for healthcare providers (physicians, nurse practitioners, and pharmacists) and allied helping professionals (midwives, medical officers, community workers, and social workers). The website incorporated information on clinical, logistical, and administrative guidance, including culturally competent and gender-affirming toolkits that could equip healthcare professionals with the requisite knowledge to provide abortion care for underserved populations. CONCLUSIONS This platform contains resources that can increase the competencies of healthcare professionals to initiate and sustain culturally and contextually appropriate abortion care for underserved groups while clarifying myths and misconceptions that often militate against initiating abortion. Our resource also has the potential to support equitable access to high-quality abortion care, particularly for those among underserved populations who may have the greatest unmet need for abortion services yet face the greatest barriers to access to 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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".