Factors associated with self-rated health in Black Canadians: A cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: Self-rated health (SRH) has shown to be a strong predictor of morbidity, functional decline, and mortality outcomes. This paper investigates the association between sociodemographic variables (e.g., employment, education, sex) and SRH among Black Canadians. METHODS: We used cross-sectional survey data (n = 1380) from the A/C (African Caribbean) Study of first- and second-generation Black Canadians in Toronto and Ottawa. Participants were invited to complete an electronic survey questionnaire in English or French in 2018-2019. Generalized linear model analyses were used to evaluate the associations among sociodemographic factors and self-rated quality of health. RESULTS: A total of 1380 self-identified Black individuals completed the survey and were included in the analysis. The majority of participants were under the age of 60 (89.7%), female (63.4%), born outside of Canada (75.1%), and residing in Toronto, Ontario (61.9%). The strongest association with poor SRH was found for difficulties accessing health care, sexual orientation, and substance misuse/disorder, while accessing/meeting basic needs was associated with better SRH, following adjustment for other socioeconomic conditions and lifestyle factors. CONCLUSION: Our findings underscore the importance of improving the social determinants of health as a conduit to improving the general health status and the quality of life of Black Canadians. Results revealed that Black Canadians may be demonstrating high levels of resilience in circumventing their current social circumstances and structural disadvantages to live the best quality of life. Understanding sociodemographic and socio-structural barriers that Black people face is essential to reducing vulnerabilities to poor outcomes and improving their health and well-being.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".