Correlates of low resilience and physical and mental well-being among Black youths in Canada
Bibliographic record
Abstract
BACKGROUND: Resilience has gained considerable attention in the mental health field as a protective factor that enables individuals to overcome mental health issues and achieve positive outcomes. A better understanding of resilience among Black youth is important for supporting the strengths and capacities within this population. This study seeks to investigate the correlates of resilience among Black youths in Canada. METHODS: The survey was conducted online through REDCap between November 2022 and March 2023. The Brief Resilience Scale (BRS) was utilized to measure the capacity of participants to recover from or bounce back from stress. The BRS comprises six five-point Likert scale items. Data were analyzed employing a bivariate analysis followed by a multivariable binary logistic regression. RESULTS: A total of 933 Black youths participated in the study across all Canadian provinces, of which 51.8% (483) identified as female and 46.7% (436) as male. Most respondents 51.3% (479) were between the ages of 16 and 20 years, with 28% (261) between the ages of 21 and 25 years, and 20.2% (188) between the ages of 26 and 30 years. In terms of employment, 62.0% (578) were working part-time, 23.7%, (220) were unemployed, and 9.8% (91) were working full-time. Over a third of participants (39.3%, 331) rated their mental health over the last month as good, with 34% (317) giving a rating of poor and 20.9% (195) giving a rating of fair. Black youths who were working part-time had four times greater odds of expressing low resilience (OR: 4.02; 95% CI: 1.82-11.29) than those who were not working. Black youth who ranked their mental health as poor were about nine times (OR: 8.65; 95% CI: 1.826-21.978) more likely to express low resilience. CONCLUSION: In this study, the Black youth participants reported relatively low resilience scores. Employment, physical health, and mental health status were factors that contributed to low resilience. Further studies are needed to examine the causal link between resilience and its dynamic effect on health outcomes among Black youth. More interventions are needed to make mental health services accessible to Black youth in a more culturally sensitive way with cross-culturally trained professionals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".