Bibliographic record
Abstract
Introduction As many as 20% of mothers experiences some type of perinatal mood and anxiety disorder (PMAD) worldwide. Women of every culture, age, income level, and race are at risk for PMADs with potential effects to mother and child. Objectives To promote awareness of maternal mental health and PMADs. Method An international task force met via online videoconference to make plans for the inaugural World Maternal Mental Health Day. The task force soon grew to include representatives from around the globe with a common goal to increase awareness of and influence policy about maternal mental health. This presentation will discuss the process, successes, challenges, and engage participants in future social marketing strategies for World Maternal Mental Health Day. International reach and impact will be discussed. Result Organizations from 12 countries were involved in this event, with twitter and landing page activity across the globe. A unique logo was developed and numerous organizations endorsed the event. An international social media campaign included a Twitter Feed “#Maternal Mental Health Matters” starting in Australia, Facebook page, and landing page. The first World MMH Day was held May 4, 2016. Conclusion Increased awareness will continue to drive social change with a goal of improving the quality of care for women worldwide who experience all types of PMADs and to reduce the stigma of maternal mental illness. World Maternal Mental Health Day will be held each year on the first Wednesday of May, close to “mother's day” and “mental health week” in many countries. Disclosure of interest The author has not supplied his/her declaration of competing interest.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.223 | 0.059 |
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".