Fulfilling the promise of digital health interventions (DHI) to promote women’s sexual, reproductive and mental health in the aftermath of COVID-19
Bibliographic record
Abstract
Globally, over 800 women die every day in pregnancy and childbirth; violence against women remains devastatingly pervasive, affecting 1 in 3 women in their lifetime, and depression rates are twice that of men, according to the World Health Organization (WHO) [ 1 ]. The report further emphasizes that sexual and reproductive health (SRH) services are quickly disrupted when health systems are under pressure which is dangerous and disempowering. Therefore, access to contraception, safe abortion to the maximum extent permitted by law, STI prevention and recovery, care and assistance for abuse survivors, and self-care interventions should all be prioritized in countries' COVID-19 responses, according to WHO [ 2 ]. As the COVID-19 pandemic paralyzes the health systems across nations, there is a significant drop in access to routine healthcare, and many patients are showing interest and turning towards telehealth, telemedicine, or remote virtual health services to access essential primary care. For example, in the United States, all the states have expanded the telehealth policies to reduce the pressure on the hospitals treating COVID-19 patients and reduce patients’ exposure [ 3 ]. Global health emergencies in the past have revealed that during the crisis, access to safe abortion can be negatively affected [ 4 ]. While countries are still grappling with COVID-19 and its response is ever-evolving. The increased burden on health systems can result in reduced access to abortion facilities. As the health systems come under mounting pressure and providers become infected, some countries have had to close down clinics offering abortion services. Such circumstances necessitate innovative solutions not only in remote places or countries with limited resources but also in developed countries [ 5 ].
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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.055 | 0.045 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".