POSTER ABSTRACTS
Bibliographic record
Abstract
Objectives: To examine disparities in access to telemedicine visits for contraception during the COVID-19 pandemic by young people's experiences of basic needs insecurity. Methods: We collected data from May 2020 to March 2021 from people at risk of pregnancy aged 18–28 in an ongoing study of community college students in California and Texas (n=1,352). Multivariate logistic regression analyses, adjusted for clustering by site, were conducted to examine differences in access to contraceptive services through telemedicine by food and housing insecurity, controlling for age, race/ethnicity, health insurance, and other key sociodemographic characteristics. Results: Only 9% of participants received their birth control method through a phone or video visit. One quarter (24%) reported it would be difficult to have a telemedicine visit for birth control. Perceived barriers to telemedicine included lacking privacy at home (42%), not knowing how to do a telemedicine visit (25%), lacking a device or Internet connection (23%), clinics not offering telemedicine (16%), and insurance not covering telemedicine (13%). Half (51%) stated they needed to get their method in person, while 36% would not feel comfortable using telemedicine, and 78% preferred in-person visits. Participants experiencing food insecurity (adjustedOR [aOR], 2.14; 95% confidence interval [CI], 1.59–2.88) and housing insecurity (aOR, 1.66; 95% CI, 1.16–2.38) were significantly more likely to report that they would have difficulty using telemedicine for birth control. Conclusions: Efforts are needed to remove barriers to telemedicine, particularly for young people facing basic needs insecurity, and to ensure that safe, high-quality in-person contraceptive services also remain accessible.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.784 | 0.571 |
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".