The Challenges of Conducting Research in Rural Populations: A Feasibility Study
Bibliographic record
Abstract
Purpose: Chronic stress related to health disparities results in negative health outcomes for mothers and infants. The brain-gut-immune axis plays a significant role in perinatal health outcomes. Researchers have not focused on the effects of rural living on the maternal/infant gut microbiome. The purpose of our study was to validate recruitment protocols, data and specimen collection protocols, participant feedback, and participant retention strategies for future studies in a rural Nebraska population of mother/infant dyads. Sample: Mother/infant dyads living in Nebraska counties with a rural-urban commuting area (RUCA) code of three or greater (n = 17 dyads, n = 1 triad). Methods: We conducted a cross-sectional pilot feasibility study by collecting stool samples, actigraph data, sleep diaries, and health and lifestyle questionnaires from mother/infant dyads living in rural Nebraska counties. Findings: Retrospective review of this pilot study identified the main feasibility findings were primarily related to distance: 1) relying on virtual recruiting methods was cost-effective; 2) stool sample shelf-life created participant inconvenience; 3) shipping carrier delays affected collection timing of actigraph data; 4) participant access to shipping carrier drop-offs increased cost and inconvenience. Conclusion: Rural locations create barriers to research, but none are insurmountable. When working with rural populations, it is important to consider the potential adaptation of participant recruitment methods and protocol procedures, including careful attention to shipping and related time constraints that may impact data collection. DOI: https://doi.org/10.14574/ojrnhc.v23i2.740
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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 | 0.299 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".