Bibliographic record
Abstract
Black women have long traditions of peer support and self-advocacy that has been advanced by the current digital age. Social media and online platforms have become spaces where Black women share and connect with other Black women, and campaign for their own needs regarding health care access and navigation. Drawing on the findings emerging from a focus group discussion between six Black women that explored their experiences in Ontario based health care settings, this paper describes women’s suggestions for increasing access to Black and women-centered virtual health-related support and advocacy. Findings reveal that despite being young, Canadian-born and university educated, anti-Black racism and sexism permeates the health care encounters of all Black women; that Black women engage in emotionally taxing labour to have their health care needs met; and that Black women’s positive and challenging experiences inform their suggestions for support and advocacy online with other Black women. The women’s visions for health care support and advocacy expose an urgency for race and gender-specific online health care support and health care reform that acknowledges the legacies of Black patients and goes beyond structural competency.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".