Empower women: examining the feasibility of using a 360° digital-first magazine as a health teaching and knowledge translation tool for millennial women
Bibliographic record
Abstract
Worldwide, women experience inequities in health due to unfair relations of power and control over their lives (Women and Gender Equity Knowledge Network, 2007). This is especially true in the area of women’s health (Husoy-Onarheim, Iversen, & Bloom, 2016; Perry, 2012). As healthcare shifts to a health promotion model, women are being empowered through the facilitation of health literacy and informed decision-making (Wuest, Merritt-Gray, Berman, & Ford-Gilboe, 2002; Leaffer & Mickelberg, 2006). In recent years, digital media has become one of the primary ways millennial women access health information (Allison, et al., 2012). Yet there are limited resources that are accurate, engaging and easy to understand (Allison, et al., 2012; Calvillo, Roman, & Roa, 2013). This project examined the feasibility of using a digital magazine as a health teaching and knowledge translation tool for millennial Canadian women. The result of this project was a pilot 360° magazine experience designed to engage millennial women in discussions about taboo health topics.
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 | 0.027 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".