Implementing Goal Mama: Barriers and Facilitators to Introducing Mobile Health Technology in a Public Health Nurse Home-Visiting Program
Bibliographic record
Abstract
The present study explores barriers and facilitators experienced by public health nurses introducing a mobile health technology platform (Goal Mama) to the Nurse-Family Partnership home-visiting program. Goal Mama is a HIPAA-compliant goal-coaching and visit preparation platform that clients and nurses use together to set and track goals. Forty-two nurses across five sites, including urban, suburban, and rural communities, piloted the platform with clients for 6 months. The mixed method, QUAL+quan pilot evaluation focused on deeply understanding the implementation process. Data were analyzed via iterative content analysis and multivariate regression analysis, and triangulated to identify salient findings. Over 6 months of use participants identified critical areas for product and implementation improvement, but still viewed the platform favorably. Key opportunities for improving sustained use revolved around supporting the technological and programmatic integration needed to lower key barriers and further facilitate implementation.
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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 | 0.013 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 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".