There’s no app for that! perspectives on engaging diverse communities to promote equitable care
Bibliographic record
Abstract
Engaging patients and communities in the development and evaluation of mHealth applications can optimize useability, user adherence, health outcomes, and transparency of how personal health data is used, stored, and transferred to commercial partners. This commentary is informed by an event that aimed to invite knowledge sharers/users from equity-denied groups to provide feedback about a preliminary set of questions meant to collect socio-structural health determinants information and their potential use within mHealth applications. Three key lessons were learned: (1) challenges of reciprocity, (2) concerns of responsible data stewardship, and (3) processes of building trust for meaningful community engagement. Responding to historical and ongoing injustices is critical for building trust and supporting successful uptake of health technologies with communities. Soliciting feedback from community once decisions about implementation have been made may come across as performative or disingenuous, further undermining possibilities to establish and maintain productive relationships that are mutually beneficial to all parties involved. Without concerted effort to improve access to healthcare resources, progress made with mHealth applications may come at the expense of people and communities already underserved within existing healthcare systems.
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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 | 0.045 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.047 |
| Scholarly communication | 0.018 | 0.030 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.032 | 0.039 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".