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Record W2065878605 · doi:10.1111/medu.12698

Community health fair with follow‐up

2015· article· en· W2065878605 on OpenAlexaff
John J. Squiers, Colin Purmal, Mandy Silver, Nora Gimpel

Bibliographic record

VenueMedical Education · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsCentre for Family Medicine
Fundersnot available
KeywordsMedicineHealth promotionAttendanceHarmCommunity healthHealth careHealth educationNursingPublic healthMedical educationFamily medicinePublic relationsEnvironmental healthPsychologyPolitical science

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.130
Threshold uncertainty score0.435

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0070.001
Scholarly communication0.0030.003
Open science0.0030.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1300.014

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.

Opus teacher head0.132
GPT teacher head0.544
Teacher spread0.412 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2015
Admission routes1
Has abstractyes

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