Searching the Internet for Infertility Information: A Survey of Patient Needs and Preferences (Preprint)
Bibliographic record
Abstract
BACKGROUND Given the complexity of infertility diagnoses and treatments and the convenience of the internet for finding health-related information, people undergoing infertility treatments often use Web-based resources to obtain infertility information and support. However, little is known about the types of information and support resources infertility patients search for on the internet and whether these resources meet their needs. OBJECTIVE The aims of this study were to (1) examine what individual factors, namely, demographic characteristics and distress, are associated with searching the internet for different types of infertility-related information and support resources and (2) determine whether Web-based resources meet the needs of patients. METHODS Men and women seeking infertility care responded to a survey assessing use of Web-based resources for accessing infertility-related information and support. The survey further assessed satisfaction with Web-based resources as well as perceived stress and depressive symptomatology. RESULTS A total of 567 participants, including 254 men and 313 women, completed the survey. Most participants (490/558, 87.8%) had searched the internet for infertility information and support. Searchers were more likely to be women (P<.001), highly educated (P=.04), long-term patients (P=.03), and more distressed (P=.04). Causes of infertility, treatment options, and scientific literature about infertility were the three most frequently searched topics, whereas ways to discuss treatment with family and friends as well as surrogacy and ways to find peer support were the three least searched topics. Of those who searched the internet, 70.9% (346/488) indicated that their needs were met by Web-based information, whereas 29.1% (142/488) said that their needs were not met. Having unmet needs was related to greater levels of perceived stress (P=.005) and depressive symptomatology (P=.03). CONCLUSIONS This study provides evidence for the important role of the internet in accessing infertility information and support and for the ability of Web-based resources to meet patients’ needs. However, although distressed patients reported particularly high rates of searching, their needs were not always met, suggesting that they may benefit from alternative sources of information and support or guidance from health care providers when searching the internet.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".