Advanced maternal age and risk perception: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Advanced maternal age (AMA) is associated with several adverse pregnancy outcomes, hence these pregnancies are considered to be "high risk." A review of the empirical literature suggests that it is not clear how women of AMA evaluate their pregnancy risk. This study aimed to address this gap by exploring the risk perception of pregnant women of AMA. METHODS: A qualitative descriptive study was undertaken to obtain a rich and detailed source of explanatory data regarding perceived pregnancy risk of 15 women of AMA. The sample was recruited from a variety of settings in Winnipeg, Canada. In-depth interviews were conducted with nulliparous women aged 35 years or older, in their third trimester, and with singleton pregnancies. Interviews were recorded and transcribed verbatim, and content analysis was used to identify themes and categories. RESULTS: Four main themes emerged: definition of pregnancy risk, factors influencing risk perception, risk alleviation strategies, and risk communication with health professionals. CONCLUSIONS: Several factors may influence women's perception of pregnancy risk including medical risk, psychological elements, characteristics of the risk, stage of pregnancy, and health care provider's opinion. Understanding these influential factors may help health professionals who care for pregnant women of AMA to gain insight into their perspectives on pregnancy risk and improve the effectiveness of risk communication strategies with this group.
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".