Promoting Culturally Respectful Cancer Education Through Digital Storytelling
Bibliographic record
Abstract
Cancer is the leading cause of mortality among Alaska Native people. Over half of Alaska Native people live in rural communities where specially trained community members called Community Health Aides/Practitioners (CHA/Ps) provide health care. In response to CHA/Ps' expressed desire to learn more about cancer, four 5-day cancer education and digital storytelling courses were provided in 2014. Throughout each course, participants explored cancer information, reflected on their personal experiences, and envisioned how they might apply their knowledge within their communities. Each course participant also created a personal and authentic digital story, a methodology increasingly embraced by Indigenous communities as a way to combine storytelling traditions with modern technology to promote both individual and community health. Opportunities to learn of CHA/Ps' experiences with cancer and digital storytelling included a 3-page end-of-course written evaluation, a weekly story-showing log kept for 4 weeks post-course, a group teleconference held 1-2 weeks post-course, and a survey administered 6 months post-course. Participants described digital storytelling as a culturally respectful way to support cancer awareness and education. Participants described the process of creating digital stories as supporting knowledge acquisition, encouraging personal reflection, and sparking a desire to engage in cancer risk reduction activities for themselves and with their families and patients. As a result of creating a personalized digital story, CHA/Ps reported feeling differently about cancer, noting an increase in cancer knowledge and comfort to talk about cancer with clients and family. Indigenous digital stories have potential for broad use as a culturally appropriate health messaging tool.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".