Defining the Black population in Canadian health research: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: In the context of health research in Canada, various terms and labels have been employed to reference the Black population. This practice has had the unintended consequence of diminishing the comparability and efficiency of studies. Furthermore, using a broad term such as 'Black' may fail to encompass the diversity and intricacy of the ethnocultural backgrounds of people who are racialised as such. It may also obscure the subtleties of their experiences and health outcomes. This study aims to examine how health researchers have defined Black within the scope of their work and different labels used to identify the Black population in Canada. METHODS AND ANALYSIS: We have developed and employed a comprehensive and sensitive search strategy to identify articles concerning the health and wellness of the Black population in Canada. Both peer-reviewed and grey literature will be searched. Original articles published in both English and French will be included. The screening process will consist of two stages: the title and abstract screening, followed by a thorough examination of full-text articles. Additionally, single citation tracking and manual search of reference lists will be conducted. Study characteristics and relevant information on the definition of the Black population will be extracted, followed by reflective thematic analysis and presentation of the key findings. ETHICS AND DISSEMINATION: This review will not require ethical approval. We will disseminate the results through meetings with stakeholders. From the beginning, a knowledge translation approach was decided upon following consultation with citizen researchers and community champions. Our findings will also be disseminated through oral and poster presentations, peer-reviewed publications, and social media.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".