Disseminating Cultural Neuropsychology Research: Five Key Recommendations for Skill Development
Bibliographic record
Abstract
Abstract Objective Cultural neuropsychology has been called upon to meet the demand for more empirical tools and frameworks to use with our diverse populations. While much is being done, we have largely been “playing a game of catch-up” (Manly, 2008) and researchers have been encouraged to reinvent their approaches (Suchy, 2016). In this regard, one area of opportunity is promoting the development of cultural neuropsychology research dissemination. Method Relevant literature and professional experiences were used to identify cultural neuropsychology research dissemination barriers and solutions. Outcomes (1) Researches should use empirically supported knowledge dissemination frameworks to guide their approaches (Wilson et al., 2010). (2) Care should be taken to report study variables in internationally compatible/meaningful units (e.g., education, socioeconomic status; UNESCOIS, 2012). Researchers are also encouraged to find opportunities to comment on the purposeful exclusion of “North-American” variables (e.g., ethnicity) as well as the lessons learned from research “failures” (Hruschka et al., 2018). (3) Findings should be presented in ways that make clinical application easily comprehensible and implementable, even for those not specializing in cultural neuropsychology (e.g., use clear titles, clarify “insider” knowledge). (4) Researchers can and should negotiate with journals to make available the translated manuscripts and supplemental materials to improve research accessibility. (5) Professional collaboration and research visibility are fundamental to the success of dissemination (Tripathy et al., 2017). Discussion Neuropsychologists are eager for more culturally informed and clinically applicable research. Thus, cultural neuropsychology researchers focusing on developing their dissemination skills in these five highlighted areas are well positioned to increase the impact of their work and promote growth within cultural neuropsychology specifically, and neuropsychology broadly. References Hruschka, D. J., Munira, S., Jesmin, K., Hackman, J., & Tiokhin, L. (2018). Learning from failures of protocol in cross-cultural research. Proceedings of the National Academy of Sciences, 115(45), 11428-11434. Manly, J. J. (2008). Critical issues in cultural neuropsychology: profit from diversity. Neuropsychological Review, 18(3), 179-183. Suchy, Y. (2016). Population-based norms in crisis. The Clinical Neuropsychologist, 30(7), 973-974. Tripathy, J. P., Bhatnagar, A., Shewade, H. D., Kumar, A. M. V., Zachariah, R., & Harries, A. D. (2017). Ten tips to improve the visibility and dissemination of research for policy makers and practitioners. Public Health Action, 7(1), 10-14. UNESCO Institute for Statistics. (2012). International Standard Classification of Education: ISCED 2011. Montreal: UNESCO Institute for Statistics. Wilson, P. M., Petticrew, M., Calnan, M. W., & Nazareth, I. (2010). Disseminating research findings: what should researchers do? A systematic scoping review of conceptual frameworks. Implementation Science, 5(1), 91.
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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 | 0.296 | 0.436 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.033 | 0.040 |
| Open science | 0.013 | 0.031 |
| Research integrity | 0.029 | 0.029 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".