Fertility patients’ use and perceptions of online fertility educational material
Bibliographic record
Abstract
BACKGROUND: Online educational information is highly sought out by patients with infertility. This study aims to assess patient-reported usage and helpfulness of fertility educational material on a clinic website and social media accounts. METHODS: Educational material was created on common fertility topics in text and video format and posted on the clinic website and social media accounts. At the first consultation for infertility, patients were provided with a postcard directing them to material online. At the first follow-up appointment, patients were invited to fill out a survey assessing whether patients viewed the online educational material and if they found the information helpful. RESULTS: 98.4% (251/255) of patients completed the survey, of which 42.6% (106/249) looked at the online material. Of those who viewed the online information, 99.1% (115/116) found the information helpful or somewhat helpful and 67.6% (73/108) found reading the material online better prepared them for making fertility decisions at their doctor's appointment. CONCLUSION: Patients found online fertility information on the clinic website and social media accounts useful for making fertility treatment decisions. Providing online educational material has the potential to improve patient care by empowering patients with the knowledge to make more informed treatment decisions, and improving the quality of the time spent with the physician.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".