“Family Medicine Needs to Be a lot more Family Medicine” – Healthcare Experiences of Black Anglophone Montrealers
Bibliographic record
Abstract
Aim/Purpose: The purpose of this study was to explore the healthcare experiences of Black anglophone Montrealers who use our public healthcare system. Background: There are many gaps when it comes to Quebec’s English-speaking Black Community (ESBC) particularly in the area of health and since this type of data is not regularly collected there is a need to find ways to understand what is going on in this community. Methodology: A qualitative approach, consisting of in-depth interviews and a focus group, was applied in order to solicit the personal experiences of the participants and thematic analysis was used to identify themes in the data set. Findings: Community support is extremely important and valuable in the ESBC and participants believe that the quality of care has eroded in the local health system over time and it is not like it used to be. The in-depth interviews also raised issues of people being dismissed for pain and attempts to be overmedicated for mental health issues. Impact on Society: Black people frequently face poor outcomes on many scales. Understanding the challenges they are facing as they navigate the health system can help us come up with solutions to help them get the quality care that they need.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".