Translating the Knowledge Gap Between Researchers and Communication Designers for Improved mHealth Research
Bibliographic record
Abstract
Our industry insight focuses on the challenges for health researchers collaborating with communication designers during the development of an App for improving maternal mental health and parenting stress. We discuss the challenges around explicating and communicating tacit and domain knowledge across disciplinary boundaries. We believe this report can widen communication design's traditional focus on users in mHealth research to consider partnerships with academic researchers. The lessons learned from our experience developing a mHealth program can be used to reduce challenges in future mHealth research, especially for collaborations between health researchers and communications designers. Considering the growth of interest in mHealth, this is extremely relevant for future team satisfaction, the optimal use of research funds and industry time, and faster development of effective mHealth tools.
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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 | 0.240 | 0.355 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.016 | 0.021 |
| Scholarly communication | 0.038 | 0.053 |
| Open science | 0.005 | 0.041 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".