Engaging youth as citizen scientists to determine health needs of New Brunswick adults
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it