Bibliographic record
Abstract
Background. Health disparities among racialized individuals and the general population have persisted throughout time. In response to global events highlighting these inequities, there have been increasing calls to collect race, ethnicity and Indigenous (REI) identity data to foster health equity. As part of this data collection, it is pivotal for organizations to ensure proper governance of this data to mitigate the potential for harm towards racialized communities. One way to safeguard this data is through the implementation of anti-racism data legislation for race-based data collected in the healthcare sector. Aim. The purpose of this study was to explore the current landscape of anti-racism data legislation for healthcare by answering the following research question: What is the justification for enacting anti-racism data legislation for race-based data collected in healthcare environments? Methods. A systematized review was conducted as it allows academic literature to be reviewed with minimal resources, while maintaining key elements of a systematic review. Results. After removing duplicates, 7625 articles underwent title and abstract screening, of which 18 articles were reviewed in full, resulting in the inclusion of one article. Implications. This study highlights the ongoing need for research centered on the benefits and challenges of implementing anti-racism data legislation for race-based data collection within healthcare, and can serve as a pilot for a larger systematic review. Keywords: anti-racism, race-based data collection, healthcare reform, legislation
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 | 0.102 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.036 | 0.029 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".