Implementation Strategies for Gender-Sensitive Public Health Practice: A European Workshop
Bibliographic record
Abstract
OBJECTIVES: Providing a robust scientific background for the focus on gender-sensitive public health and a systematic approach to its implementation. METHODS: Within the FP7-EUGenMed project ( http://eugenmed.eu ) a workshop on sex and gender in public health was convened on February 2-3, 2015. The experts participated in moderated discussion rounds to (1) assemble available knowledge and (2) identify structural influences on practice implementation. The findings were summarized and analyzed in iterative rounds to define overarching strategies and principles. RESULTS: The participants discussed the rationale for implementing gender-sensitive public health and identified priorities and key stakeholders to engage in the process. Communication strategies and specific promotion strategies with distinct stakeholders were defined. A comprehensive list of gender-sensitive practices was established using the recently published taxonomy of the Expert Recommendations for Implementing Change (ERIC) project as a blueprint. CONCLUSIONS: A clearly defined implementation strategy should be mandated for all new projects in the field of gender-sensitive public health. Our tool can support researchers and practitioners with the analysis of current and past research as well as with the planning of new projects.
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 | 0.202 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".