“Who needs an app? Fertility patients’ use of a novel mobile health app”
Bibliographic record
Abstract
Objective The number of couples experiencing infertility treatment has increased, as has the number of women and men experiencing infertility treatment-related stress and anxiety. Therefore, there is a need to provide information and support to both men and women facing fertility concerns. To achieve this goal, we designed a mhealth app, Infotility, that provided men and women with tailored medical, psychosocial, lifestyle, and legal information. Methods This study specifically examined how fertility factors (e.g. time in infertility treatment, parity), socio-demographic characteristics (e.g. gender, education, immigrant status), and mental health characteristics (e.g. stress, depression, anxiety, fertility-related quality of life) were related to male and female fertility patients’ patterns of use of the Infotility app. Results Overall, the lifestyle section of the app was the most highly used section by both men and women. In addition, women without children and highly educated women were more likely to use Infotility. No demographic, mental health or fertility characteristics were significantly associated with app use for men. Conclusion This study shows the feasibility of a mhealth app to address the psychosocial and informational needs of fertility patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.009 | 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".