Abstract PO-120: Need for more sociodemographic data in qualitative childhood cancer research: Findings from a scoping review
Bibliographic record
Abstract
Abstract Childhood cancer is increasingly recognized as a global health priority. In comparison with other childhood diseases, researchers have long- emphasized–the need to include children in empirical studies on cancer diagnosis, treatment, and recovery, particularly through the use of qualitative methods (Bluebond-Langner 1978). Children's inclusion originated from the theoretical perspective that adults may not understand the needs of children and that, to understand children's perspectives, we must engage and involve them in the research process. Given the new focus on global childhood cancer, we ask if and how sociodemographic factors, such as age, ethnicity, financial status and other categories, have been included in qualitative research with children who have cancer. Reporting ofn the inclusion and analysis of these factors is necessary to address disparities; research on adult cancer has shown that sociodemographic factors shape experiences with cancer diagnosis, treatment, and survival. The authors conducted a scoping review of qualitative studies involving children in cancer research between 2007-2019. Articles were retrieved from Ovid Medline, Embase, and CINAHL. Article titles and abstracts were screened and included in full text review if they were cancer related, used qualitative methods, and included participants aged 6 to 11. Additional articles that met the inclusion criteria but were found after the database search were considered and coded. A total of 88 articles were screened, with 76 articles met the inclusion criteria the full text retrieved for coding. Articles were coded for reported sociodemographic factors of children and their caretakers involved in the study. Further, each study's identified sociodemographic categories were analyzed for their significance/importance to the respective study. Results show gaps in sociodemographic reporting and analysis (e.g., factors such as race, education, financial status not reported or included in analysis), little research being conducted in under-resourced areas (e.g., studies outside of the US, Canada, Europe), and frameworks used in the reviewed articles exclude the social/environmental context as a contributing factor in medical care. With the popular frameworks in current childhood studies, including Humanistic Nursing Theory and phenomological theory, adding sociodemographic reporting and analysis can contribute to childhood cancer research by increasing the variety and number of children to whom the research findings will be contextually relevant. Reporting the inclusion of these factors in children's cancer research is needed to highlight populations who have been traditionally underrepresented, and to identify areas for future research examining how sociodemographic factors impact the cancer care experience. We hope in identifying these gaps and opportunities for future research, this work will invite childhood cancer research to expand its reach and combat disparities that exist within cancer diagnosis, care, and survival. Citation Format: Sarah Burack, Eric M. Wiedenman, Melanie Ward, Lindsay Kaufman, Thembekile Shato, Jean Hunleth. Need for more sociodemographic data in qualitative childhood cancer research: Findings from a scoping review [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-120.
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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 | 0.103 | 0.354 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.026 | 0.030 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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".