Abstract 60: Cancer Navigator: A Video-Based Support and Education Tool for Cancer Patients and Their Caregivers
Bibliographic record
Abstract
Abstract Purpose: The aim of this study was to identify the informational needs of cancer patients and caregivers among a diverse population in Colombia to inform the development of Cancer Navigator, a video-based tool to help cancer patients and their families understand and navigate their treatment. Methods: Seven focus group discussions (FGDs) with n=47 participants and n=80 in-depth interviews (IDIs) were conducted to understand the needs of cancer patients and caregivers regarding their cancer diagnosis, treatment and rehabilitation in the Colombian healthcare system. The information was analyzed using thematic analysis, and informed the development of a series of 18 short infographic and personal narrative videos covering different cancer topics, delivered by local clinicians and survivors; along with a resource guide of local services for food, housing and transportation. A pilot study was conducted to evaluate pre-post depression and anxiety among 22 caregivers and 18 cancer patients at Hospital Méderi (Bogotá, Colombia). Results: Themes/topics identified from the interviews included: treatment effects; cancer etiology; nutrition, physical activity recommendations; psychological/spiritual/emotional support; alternative therapies; warning signs; self-learning cancer tools and administrative procedures. The pilot study showed a mild decrease in anxiety and distress immediately after viewing three videos. The proportion of patients with anxiety levels above normal (>7 on Hospital Anxiety and Depression Scale, HADS) changed from 27% to 15% after viewing (p=0.17), and distress (score >5 on the distress thermometer) decreased from 35% to 22% (p=0.22). Conclusion: The Cancer Navigator video-based educational/support tool shows promise for reducing anxiety and distress among cancer patients and caregivers in Colombia. Next steps will be to test the feasibility and acceptability of the videos among patients, caregivers and healthcare providers at three additional oncology hospitals in Bogotá in 2021. Citation Format: Ana Pedraza-Flechas, Carolyn Taylor, Erika Barrera-Suárez, Luis Perez, Jesica Soto, Alejandra Arias, Andres Patiño, Vera Perales, Ángela Pinzón-Rondon, Ángela Ruíz-Sternberg, Anna Cabanes, Phuongthao Le. Cancer Navigator: A Video-Based Support and Education Tool for Cancer Patients and Their Caregivers [abstract]. In: Proceedings of the 9th Annual Symposium on Global Cancer Research; Global Cancer Research and Control: Looking Back and Charting a Path Forward; 2021 Mar 10-11. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2021;30(7 Suppl):Abstract nr 60.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".