052 Co-creating a health decision aid with immigrant women in Quebec (Cohda – immigrant women)
Bibliographic record
Abstract
Introduction Intersectional factors, notably the immigratory situation, significantly impact women’s health. This study aims to assess the specific health decisional needs of immigrant women in Quebec, Canada, and collaborate with them to develop a tailored decision aid (DA). Methods Employing an Integrated Knowledge Translation approach, a steering committee comprising immigrant women, community-based organizations (CBOs), policymakers, health professionals, and researchers will be established. Following the Ottawa Decision Support Framework, interviews will be conducted to elucidate immigrant women’s health decisional needs. Additionally, a systematic review will identify existing DAs, evaluating their applicability within the targeted population and context. The most promising DA will be refined, incorporating new evidence as necessary, and uploaded onto the PADA collaborative platform for feedback from knowledge users. Subsequently, after integrating suggestions, a prototype of the DA will be developed. Preliminary Results Initial engagements with potential steering committee members are in progress, with two CBOs committed to participation. They have underscored the pressing need for DAs customized to suit the nuanced challenges faced by immigrant women. Challenges, such as varying levels of vulnerability among women of diverse immigration statuses (e.g., refugees, temporary residents), along with individual socio-economic backgrounds and experiences (e.g., literacy, domestic violence), have been highlighted. Discussion The immigration status profoundly influences women’s access to and experiences within health and social services, thereby shaping their decisional needs. Recognizing and addressing the diverse personal, cultural, and socio-economic backgrounds of these women is pivotal in facilitating shared decision-making within this demographic. Conclusions Establishing collaborative partnerships with field experts who comprehend the multifaceted challenges of the real context is imperative for co-producing tailored DAs aimed at addressing the health decisional needs of immigrant women.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".