Community health fair with follow‐up
Bibliographic record
Abstract
Medical and health professions schools commonly host community health fairs staffed by students. Fairs typically provide patrons with free health screenings and health education. These events allow students opportunities to practise health promotion, while forging relationships with their local communities. Although health fairs appear to offer patrons many benefits with little risk for harm, these events nevertheless require review to ensure they are established ethically and that their benefits outweigh any risks.1 Potential risks include the instigation of expensive tests indicated by false positive screenings and the delivery of false negative results. As our institution's annual health fair in a historically medically underserved community grew in both attendance and scope, we hoped to ensure our patrons benefited from the services we provide. Unfortunately, assessment of health fair successes in the literature is largely limited to measures of outputs (e.g. number of people screened), and rarely are actual health outcomes following fairs reported. We decided to investigate whether our health fair was ethically and sustainably implemented to promote tangible benefits. Student leaders of our annual health fair developed an institutional review board-approved health awareness programme (HAP) to establish follow-up contact with fair patrons. The HAP committee recruits medical and health professions students throughout the year, and these students receive monthly training in health promotion and disease prevention. At the fair, these volunteers identify patrons at increased risk for adverse cardiovascular events through self-report surveys and connect them with local health care providers and community clinics. These patrons undergo an informed consent process to protect their autonomy and privacy. HAP volunteers then contact study subjects quarterly to track their progress in finding access to medical care and to collect information about outcomes following the fair. Patrons who have not visited a doctor or clinic are offered more information and assistance in accessing medical services. Ultimately, the goal of the HAP is to sustain data-driven follow-up that expands the impact of our annual fair. Through HAP follow-up, we have learned that our fair can have a measurable, positive impact on members of the community it serves. Data from our most recent fair revealed that after 1 month 30% of patrons had already scheduled a doctor's appointment and 65% planned to schedule one. However, nearly 75% of those planning to schedule an appointment wanted help in finding a clinic and making an appointment. Although patrons had been given information about scheduling appointments at community clinics on the day of the fair, the majority requested additional help 1 month later. After 6 months, 65% of those contacted had visited a doctor, and 57% of those who had done so had been given a new diagnosis. Without HAP follow-up, many patrons might never have visited a doctor after the fair. The HAP committee will continue to improve this model to ensure patrons screened at the fair successfully establish continuity of care.
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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 | 0.022 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.130 | 0.014 |
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".