“Who needs an app? Fertility patients’ use of a novel mobile health app”
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it