Risky Business: Increasing Fertility Knowledge of Men in the General Public Using the Mobile Health Application <i>Infotility XY</i>
Bibliographic record
Abstract
Male infertility presents a public health concern. As most men wish to become fathers, it is important to increase men’s awareness of infertility risk factors. We developed a mobile health application (mHealth app), Infotility XY, to promote men’s reproductive health. This study evaluates whether use of the app led to increased knowledge of infertility risk factors, and whether knowledge change was associated with participants’ sociodemographic characteristics and/or app usage. Participants were recruited between August and October 2020. Eligibility criteria included: identified as male; 18–45 years old; childless; no infertility history; able to read and write in English/French; had internet access. We assessed participants’ fertility knowledge before and after app use. App usage data were captured during the 2-week intervention period. Our sample included 49 men aged 18–45. Seventy-eight percent of participants had not previously sought fertility information. Participants viewed on average 75% of the app’s articles, and 96% of participants said the app increased their fertility knowledge. Before app use, 55% of men said they were aware of infertility risk factors, compared to 96% after app use. Men correctly identified more risk factors after app use compared to before, t(48) = 8.28, p < .001. Participants’ sociodemographic characteristics and amount of app usage were not associated with knowledge change. This study provides evidence of the feasibility of an mHealth app to improve men’s awareness of infertility risk factors. Given the positive relationship between male reproductive health and overall health, increased awareness of infertility risk factors may lead to men’s improved overall health.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".