MétaCan
Menu
Back to cohort
Record W3157514169 · doi:10.1177/23333936211014497

Implementing Goal Mama: Barriers and Facilitators to Introducing Mobile Health Technology in a Public Health Nurse Home-Visiting Program

2021· article· en· W3157514169 on OpenAlexaff
Arianna Taboada, Elizabeth S. Ly, Danielle E. Ramo, Fred Dillon, Yin-Juei Chang, Clare Hooper, Elly Yost, Jana Haritatos

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2021
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsAyogo (Canada)
Fundersnot available
KeywordsCoachingGeneral partnershipNursingPublic healthProcess (computing)Key (lock)Mobile technologyMedical educationMedicinePsychologyBusinessMobile deviceComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.099
GPT teacher head0.561
Teacher spread0.462 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Qualitative Nursing ResearchSame topicTelemedicine and Telehealth ImplementationFrench-language works237,207