Utilizing RE-AIM to scope potential for feasible immigrant cancer literacy education
Bibliographic record
Abstract
Disparities in cancer incidence and mortality exist between settled and newly-arrived immigrant communities in immigrant-nations, such as Australia, Canada and USA. This may be due to differences in the uptake of cancer prevention behaviours and services for early detection, and cultural, language or literacy barriers impacting understanding of mainstream health messages. Blending cancer-literacy with immigrant English language education presents a promising means to reach new immigrants attending language programs. Guided by the RE-AIM framework for translational research, this study explored the feasibility and translation potential of this approach within the Australian context. Focus groups and interviews (N = 22) were held with English-as-a-Second-Language (ESL) teachers and immigrant resource-centre personnel. Thematic Framework Analysis, driven by RE-AIM, identified potential barriers to Reach for immigrants, Adoption by teachers, Implementation into immigrant-language programs and long-term curriculum Maintenance. Responses further highlighted that an Efficacious ESL cancer-literacy resource could be facilitated by developing flexible, culturally-sensitive content to cater for multiple cultures. Interviewees also raised the importance of developing the resource according to national curricula-frameworks, different language levels, and incorporating varied communicative activities and media. This study therefore offers insight into potential barriers and facilitators to developing a resource feasible for inclusion in existing immigrant-language programs, and achieving reach to multiple communities.
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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.009 |
| 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".