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Record W3128318643 · doi:10.1186/s12911-021-01406-z

Development of a novel mobile application, HBB Prompt, with human factors and user-centred design for Helping Babies Breathe skills retention in Uganda

2021· article· en· W3128318643 on OpenAlexafffund
Natalie Chan, Hasan S. Merali, Niraj Mistry, Ryan Kealey, Douglas M. Campbell, Shaun K. Morris, Data Santorino

Bibliographic record

VenueBMC Medical Informatics and Decision Making · 2021
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsHospital for Sick ChildrenTD Bank GroupMcMaster Children's HospitalUniversity of TorontoB.C. Women's Hospital & Health Centre
FundersGrand Challenges CanadaCenter for Arthropod Management Technologies
KeywordsFocus groupForgettingMedicineMedical educationPeer learningNursingPsychologyPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Helping Babies Breathe (HBB) is a life-saving program that has helped reduce neonatal morbidity and mortality, but knowledge and skills retention after training remains a significant challenge for sustainability of impact. User-centred design (UCD) can be used to develop solutions to target knowledge and skills maintenance. METHODS: We applied a process of UCD beginning with understanding the facilitators of, and barriers to, learning and retaining HBB knowledge and skills. HBB Master Trainers and frontline HBB providers participated in a series of focus group discussions (FGDs) to uncover the processes of skills acquisition and maintenance to develop a mobile application called "HBB Prompt". Themes derived from each FGD were identified and implications for development of the HBB Prompt app were explored, including feasibility of incorporating strategies into the format of an app. Data analysis took place after each iteration in Phase 1 to incorporate feedback and improve subsequent versions of HBB Prompt. RESULTS: Six HBB trainers and seven frontline HBB providers participated in a series of FGDs in Phase 1 of this study. Common themes included lack of motivation to practise, improving confidence in ventilation skills, ability to achieve the Golden Minute, fear of forgetting knowledge or skills, importance of feedback, and peer-to-peer learning. Themes identified that were not feasible to address pertained to health system challenges. Feedback about HBB Prompt was generally positive. Based on initial and iterative feedback, HBB Prompt was created with four primary functions: Training Mode, Simulation Mode, Quizzes, and Dashboard/Scoreboard. CONCLUSIONS: Developing HBB Prompt with UCD to help improve knowledge and skills retention was feasible and revealed key concepts, including drivers for successes and challenges faced for learning and maintaining HBB skills. HBB Prompt will be piloted in Phase 2 of this study, where knowledge and skills retention after HBB training will be compared between an intervention group with HBB Prompt and a control group without the app. Trial registration Clinicaltrials.gov (NCT03577054). Retrospectively registered July 5, 2018, https://clinicaltrials.gov/ct2/show/study/NCT03577054 .

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.329
Teacher spread0.282 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations8
Published2021
Admission routes2
Has abstractyes

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