Community Agency Health Promotion Capacity for Ethno-Culturally Diverse Immigrant Women: Qualitative Interviews
Bibliographic record
Abstract
High rates of international migration must be addressed by healthcare systems. In particular, immigrant women lack access to and quality of care. Community-based health promotion may be one way to reach immigrant women. The aim of this study was to explore the capacity of immigrant settlement agencies for health promotion to immigrant women. We conducted semi-structured telephone interviews with immigrant women and community agency managers to discuss current and required health promotion capacity based on the New South Wales Framework, and identified themes using content analysis. We interviewed 24 immigrant women and 22 staff from 20 immigrant settlement agencies across Canada. Women and agency staff largely agreed on the need to develop the workforce (staff type and qualifications), acquire resources (human, physical, financial) dedicated to health promotion, establish external partnerships with academic, healthcare and other community organizations, create policies and strategies specific to health promotion, and choose and train leaders with interpersonal and technical skills. In addition, women underscored the need to tailor health promotion programs and services to women, and to enhance access to community-based health promotion by raising awareness via diverse media and government settlement agencies, and supporting participation by paying for transportation to community agencies or providing programs and services virtually or in multiple convenient locations. Action is needed to integrate these findings into policy that supports community-based health promotion, and into community agency policies and strategies. Ongoing research is needed to establish optimal community agency health promotion models and impact. Ultimately, community agency health promotion may reduce healthcare inequities, and lead to improved health and wellness among immigrant women.
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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".