Black disabled people and mental health: Intersectionality of racism, COVID-19, and disability. An autoethnographic journey.
Bibliographic record
Abstract
Abstract Mental, physical, and emotional health are essential for everyone to enable the totality and completeness of one’s health and well-being to enhance quality of life. Mental health is one of the deep-rooted issues in the racialized community. Among Black disabled people, mental health is among some neglected areas in research. Generally, there is a lack of culturally appropriate mental health promotion and advocacy for Black disabled people. Due to my professional experience in the health, social services and community non-profit organisations, I was motivated to utilize auto ethnographic methodology to explore my personal experiences, thoughts and ideas regarding the intersectionality of race, COVID-19 and disability and how these interconnected factors affect the mental health of Black disabled people. In this study, I performed reflective examination of my ideas and thoughts revealing how race, COVID-19 and disability have negatively impacted mental health of Black disabled people. I also relied on my own memory of events from my interaction with disabled people, participant observation field notes and research dairies as data collection techniques. Within my stories, I addressed how Black disabled people experienced racism and COVID-19 as a dual pandemic that impact mental health. As the growing awareness of negative impact of the pandemic on Black disabled people widens, I provided readers with possible strategies and recommendations that might solve the impact of racism and COVID-19 on the mental health of Black disabled people. I recommended that mental health training package, toolkit and resources about Black disabled people should be widely distributed and used by everyone who want to embark on the critical journey toward greater awareness, implementation of human rights and efficient creation of an inclusive society.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| 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".