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Record W4390552500 · doi:10.2196/53439

Administrative Dashboard for Monitoring Use of a Web-Based Parent Training Intervention: Usability Study

2024· article· en· W4390552500 on OpenAlexvenueno aff
Susan M. Breitenstein, Julia Berteletti, Shea Smoske, Charles Barger, Kyrie Tipps, Nathan Helsabeck

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human Development
KeywordsPreprintUsabilityDashboardIntervention (counseling)World Wide WebComputer sciencePsychologyHuman–computer interactionMedicineData scienceNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Web-based parent training (PT) programs can strengthen parent-child relationships by equipping caregivers with knowledge and evidence-based strategies to manage behavior. Hybrid facilitation of PT includes facilitator interaction paired with self-administered and web-based PT. Web-based administrative dashboards provide users (eg, administrators, facilitators, and researchers) with an integrated platform to monitor parent progress and activities within a PT program or website. Despite the utility and prevalence of administrative dashboards for web-based behavioral interventions, to our knowledge, no research studies have explored the perspectives and insights of dashboard users to enhance user experience and program delivery. OBJECTIVE: The purpose of this study is to evaluate the usability of the administrative dashboard (ezDashboard) for the ezParent program, a 6-module web-based PT program for parents of children aged 2-5 years. METHODS: This study used a descriptive, single-group design with administrators who were overseeing the implementation of the ezParent program and trained facilitators for hybrid ezParent delivery. Participants spent at least 30 minutes reviewing and evaluating the ezDashboard and then completed a survey of their experience with the dashboard. The survey included the validated 10-item System Usability Scale and open-ended questions focusing on user performance, navigation ease, and overall usefulness of the ezDashboard. RESULTS: Participants (N=15) indicated high usability of the ezDashboard with System Usability Scale scoring a total mean score of 83.5 (SD 16.3). Most participants (n=13, 87%) rated the overall user-friendliness of the ezDashboard as good (n=3, 20%), excellent (n=9, 60%), or best imaginable (n=1, 7%). Open-ended questions revealed the ezDashboard is or would be useful to monitor parent progress and trends in engagement (n=8, 53%) and for reviewing topics for discussion and communicating with parents (n=5, 33%). ezParent administrators (n=4) identified that real-time data for ezParent use helps overall management of program uptake. Suggestions for features to add to the ezDashboard included the ability to track partial progress of program modules (4/14, 29%), total time spent per module (2/14, 14%), and exportable reports (4/14, 29%). Other ideas for improvement included direct messaging capabilities, videoconferencing platform integration, and being able to modify participant account and contact information. CONCLUSIONS: Results indicate that the ezDashboard is easy to use and provides functional information to facilitators and administrators in delivering ezParent. Qualitative results indicate that integrating suggested features into the ezDashboard may help provide a smoother experience for facilitators, administrators, and ultimately the parents using the program. Providing resources for facilitators and administrators in real time to monitor intervention participants' progress in a program can be helpful in tracking progress and providing facilitated support in tailoring program content and program completion.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.327
GPT teacher head0.517
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2024
Admission routes1
Has abstractyes

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