Media representation of African individuals in Australia during the COVID-19 pandemic and its impact on mental health
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the impact of media representation on the mental health of Australians of African descent during the COVID-19 pandemic. By analysing the media coverage of COVID-19 restriction breaches, particularly focusing on individuals from African backgrounds, the study aims to shed light on how racially charged narratives can contribute to emotional distress and exacerbate feelings of alienation within these communities. The findings highlight the detrimental effects of such portrayals, emphasising the need for more responsible and inclusive media reporting to safeguard the mental well-being of culturally and linguistically diverse populations. Design/methodology/approach The study employed media content analysis to explore representations of Australians of African origin versus the broader Anglo–Australian population during the COVID-19 pandemic, focusing on racial identity’s impact on news coverage of COVID-19 restriction breaches. Researchers classified and distilled extensive textual content, using a diverse sample from various ethnic-racial backgrounds, with an emphasis on African Australians within the CALD community. Data analysis was conducted using NVivo (version 12) software, following an inductive approach. Findings The findings underscore the consistent portrayal of people from African communities as outsiders and the racial profiling they experience in media coverage of significant issues like COVID-19. Originality/value There is very limited research that examines the impact of media coverage on African migrants during the COVID-19 pandemic.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".