The inhibitors and enablers of emerging adult COVID-19 mitigation compliance in a township context
Bibliographic record
Abstract
Young adults are often scapegoated for not complying with COVID-19 mitigation strategies. While studies have investigated what predicts this population’s compliance and non-compliance, they have largely excluded the insights of African young people living in South African townships. Given this, it is unclear what places young adult South African township dwellers at risk for not complying with physical distancing, face masking and handwashing, or what enables resilience to those risks. To remedy this uncertainty, the current article reports a secondary analysis of transcripts (n=119) that document telephonic interviews in June and October 2020 with 24 emerging adults (average age: 20 years) who participated in the Resilient Youth in Stressed Environments (RYSE) study. The secondary analysis, which was inductively thematic, pointed to compliance being threatened by forgetfulness; preventive measures conflicting with personal/collective style; and structural constraints. Resilience to these compliance risks lay in young people’s capacity to regulate their behaviour and in the immediate social ecology’s capacity to co-regulate young people’s health behaviours. These findings discourage health interventions that are focused on the individual. More optimal public health initiatives will be responsive to the risks and resilience-enablers associated with young people and the social, institutional, and physical ecologies to which young people are connected. Significance: Emerging adult compliance with COVID-19 mitigation strategies is threatened by risks across multiple systems (i.e. young people themselves; the social ecology; the physical ecology). Emerging adult resilience to compliance challenges is co-facilitated by young people and their social ecologies. Responding adaptively to COVID-19 contagion threats will require multisystem mobilisation that is collaborative and transformative in its redress of risk and co-championship of resilience-enablers. Open data set: https://doi.org/10.25392/leicester.data.17129858
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".