A systematic review of culturally sensitive cancer prevention resources for ethnic minorities.
Bibliographic record
Abstract
BACKGROUND: Cancer prevention educational resources intended for members of ethnically diverse populations should be tailored to the specific cancer knowledge and beliefs of individual ethnic groups. Culturally sensitive printed materials on cancer prevention have been proposed in the literature. OBJECTIVES: 1) To explore definitions of the term cultural sensitivity (CS) and their application to the development and testing of cancer prevention education materials for ethnic minority groups; and 2) to assess the use of instruments or scales used to measure the CS of cancer information resources. DESIGN: A list of articles explicitly on the CS of cancer prevention education materials was assembled by searching the databases of PubMed, CancerLit, PsycINFO, and Sociological Abstracts for articles published between 1994-2004. RESULTS: Ten studies that met inclusion criteria were included in this review. Most articles included breast cancer resources (90%) and targeted African American populations (70%). Few studies defined the term CS (n=4). Only three studies employed instruments to evaluate the CS of printed cancer information resources, and none of them explicitly listed standard measures of validity or reliability. CONCLUSIONS: Best practice definitions and guidelines for culturally sensitive cancer prevention education need to be established. Ethnic minority individuals' cancer-related knowledge and beliefs must be incorporated into all printed cancer education efforts.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".