Identification of priorities for multidisciplinary intersectoral research on violence against older women
Bibliographic record
Abstract
Sarah D Kosa,1,2 Sheila Macdonald,1 Maureen Etkin,3 Raeann Rideout,3 Janice Du Mont2,4 1Ontario Network of Sexual Assault/Domestic Violence Treatments Centres, Toronto, ON, Canada; 2Women’s College Research Institute, Women’s College Hospital, Toronto, ON, Canada; 3Elder Abuse Ontario, Etobicoke, ON, Canada; 4Dalla Lana School of Public Health, University of Toronto, Toronto, ON, CanadaIt has been acknowledged globally and in Canada that to address the growing and significant health and social problem of violence against older women, it is essential that key stakeholders work collaboratively across sectors and disciplines to identify research priorities, as well as methodological approaches and potential challenges to consider in moving these priorities forward.1–3 To this end, we engaged leaders of professional health care associations, managers of health, social, and community services, government policymakers, and researchers in a two-step Delphi consensus process in Canada’s largest province, Ontario.4–6
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 | 0.228 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.035 | 0.017 |
| Scholarly communication | 0.029 | 0.015 |
| Open science | 0.006 | 0.042 |
| Research integrity | 0.008 | 0.013 |
| 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".