From English to “Englishes”: A Process Perspective on Enhancing the Linguistic Responsiveness of Culturally Tailored Cancer Prevention Interventions
Bibliographic record
Abstract
Linguistic accommodation refers to the process of adjusting one's language, speech, or communication style to match or adapt to that of others in a social interaction. It is known to be vital to effective health communication. Despite this evidence, there is little scientific guidance on how to design linguistically adapted health behavior interventions for diverse English-speaking populations. This study aims to document the strategies used to develop a culturally grounded cancer prevention intervention with the capabilities to linguistically accommodate to speakers of African American English (AAE). We describe the iterative process of developing a cancer prevention intervention with contributions of racially and linguistically diverse colleagues representing various community and institutional perspectives, including communication scientists, linguists, a community advisory board, professional voice talents, and institutional representatives for scientific integrity. We offer a detailed description of the successes and, in some cases, failures of strategies. Social stereotypes associated with AAE were prevalent at both institutional and community levels, resulting in unanticipated challenges and delays during intervention development. The diversity of linguistic, racial, and role identities within the message development team was integral to successfully addressing and identifying opportunities for process improvement. Language is a vital but often overlooked aspect of intervention development. Message designers should consider implicit social stereotypes that unintentionally shape linguistic choices. This study provides a novel overview of how various types of expertise and iterative message development processes contribute to successfully navigating cultural grounding when sensitive or stigmatized issues are salient.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".