Widening the gap? Unintended consequences of health promotion measures for young people during COVID-19 lockdown
Bibliographic record
Abstract
During the first wave of the COVID-19 pandemic, global measures preventing the spread of the new coronavirus required most of the population to lockdown at home. This sudden halt to collective life meant that non-essential services were closed and many health promoting activities (i.e. physical activity, school) were stopped in their tracks. To curb the negative health impacts of lockdown measures, activities adapting to this new reality were urgently developed. One form of activity promoted indoor physical activity to prevent the adverse physical and psychological effects of the lockdown. Another form of activity included the rapid development of online learning tools to keep children and youth engaged academically while not attending school. While these health promoting efforts were meant to benefit the general population, we argue that these interventions may have unintended consequences and inadvertently increase health inequalities affecting marginalized youth in particular, as they may not reap the same benefits, both social and physical, from the interventions promoting at-home physical activities or distance learning measures. We elaborate on several interventions and their possible unintended consequences for marginalized youth and suggest several strategies that may mitigate their impact.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".