Online Health Information Regarding Male Infertility: An Evaluation of Readability, Suitability, and Quality
Bibliographic record
Abstract
BACKGROUND: Many men lack knowledge about male infertility, and this may have consequences for their reproductive and general health. Men may prefer to seek health information online, but these sources of information vary in quality. OBJECTIVE: The objective of this study is to determine if online sources of information regarding male infertility are readable, suitable, and of appropriate quality for Internet users in the general population. METHODS: This study used a cross-sectional design to evaluate online sources resulting from search engine queries. The following categories of websites were considered: (1) Canadian fertility clinics, (2) North American organizations related to fertility, and (3) the first 20 results of Google searches using the terms "male infertility" and "male fertility preservation" set to the search locations worldwide, English Canada, and French Canada. Websites that met inclusion criteria (N=85) were assessed using readability indices, the Suitability Assessment of Materials (SAM), and the DISCERN tool. The associations between website affiliation (government, university/medical, non-profit organization, commercial/corporate, private practice) and Google placement to readability, suitability, and quality were also examined. RESULTS: None of the sampled websites met recommended levels of readability. Across all websites, the mean SAM score for suitability was 45.37% (SD 11.21), or "adequate", while the DISCERN mean score for quality was 43.19 (SD 10.46) or "fair". Websites that placed higher in Google obtained a higher overall score for quality with an r (58) value of -.328 and a P value of .012, but this position was not related to readability or suitability. In addition, 20% of fertility clinic websites did not include fertility information for men. CONCLUSIONS: There is a lack of high quality online sources of information on male fertility. Many websites target their information to women, or fail to meet established readability criteria for the general population. Since men may prefer to seek health information online, it is important that health care professionals develop high quality sources of information on male fertility for the general 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 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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".