COVID-19 Information Sources and Health Behaviors During Pregnancy: Results From a Prenatal App-Embedded Survey
Bibliographic record
Abstract
Background Pregnancy is a time of heightened COVID-19 risk. Pregnant individuals’ choice of specific protective health behaviors during pregnancy may be affected by information sources. Objective This study examined the association between COVID-19 information sources and engagement in protective health behaviors among a pregnant population in a large academic medical system. Methods Pregnant patients completed an app-based questionnaire about their sources of COVID-19 information and engagement in protective health behaviors. The voluntary questionnaire was made available to patients using a pregnancy app as part of their routine prenatal care between April 21 and November 27, 2020. Results In total, 637 pregnant responders routinely accessed a median of 5 sources for COVID-19 information. The most cited source (79%) was the Centers for Disease Control and Prevention (CDC). Self-reporting evidence-based protective actions was relatively common, although 14% self-reported potentially harmful behaviors to avoid COVID-19 infection. The CDC and other sources were positively associated with engaging in protective behaviors while others (eg, US president Donald Trump) were negatively associated with protective behaviors. Participation in protective behaviors was not associated with refraining from potentially harmful behaviors (P=.93). Moreover, participation in protective behaviors decreased (P=.03) and participation in potentially harmful actions increased (P=.001) over the course of the pandemic. Conclusions Pregnant patients were highly engaged in COVID-19–related information-seeking and health behaviors. Clear, targeted, and regular communication from commonly accessed health organizations about which actions may be harmful, in addition to which actions offer protection, may offer needed support to the pregnant population.
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 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".