Engaging youth as citizen scientists to determine health needs of New Brunswick adults
Bibliographic record
Abstract
Community health needs assessments (CHNAs) are important tools to determine community health needs, however, populations that face inequities may not be represented in existing data. The use of mixed methods becomes essential to ensure the needs of underrepresented populations are included in the assessment. We created an in-school public health course where students acted as citizen scientists to determine health needs in New Brunswick, New Jersey adults. By engaging members of their own community, students reached more representative respondents and health needs of the local community than a CHNA completed by the academic hospital located in the same community as the school which relies on many key health statistics provided at a county level. New Brunswick adults reported significantly more discrimination, fewer healthy behaviors, more food insecurity, and more barriers to accessing healthcare than county-level participants. New Brunswick participants had significantly lower rates of health conditions but also had significantly lower rates of health screenings and higher rates of barriers to care. Hospitals should consider partnering with local schools to engage students to reach populations that face inequities, such as individuals who do not speak English, to obtain more representative CHNA data.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".