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Record W3124155422 · doi:10.2196/23921

Tablet-Based Apps for Phonics and Phonological Awareness: Protocol for Evidence-Based Appraisal of Content, Quality, and Usability

2021· article· en· W3124155422 on OpenAlexvenueno aff
Lisa Furlong, Tanya Serry, Shane Erickson, Meg E. Morris

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilityProtocol (science)Computer scienceContext (archaeology)Reading (process)Quality (philosophy)Systematic reviewMedical educationWorld Wide WebMedicineMEDLINE

Abstract

fetched live from OpenAlex

BACKGROUND: The use of mobile apps to manage and promote health is becoming increasingly popular. Mobile apps are gaining popularity particularly in educational and interventional settings owing to their perceived advantages including support for and engagement of individuals with reading difficulties. In the context of COVID-19, the need for technology-based tools has increased. For practitioners and educators who wish to use apps in their practice or recommend apps to individuals with reading difficulties, it is challenging to identify high-quality apps in app stores. OBJECTIVE: This protocol describes a systematic search, selection, and appraisal process for tablet apps targeting phonics knowledge and phonological awareness skills. This protocol aimed to (1) provide a systematic method for identifying tablet apps targeting phonics knowledge and phonological awareness skills in the Google Play Store and Apple's App Store and (2) describe an evidence-based approach for quality appraisal of these apps by using structured tools. METHODS: This protocol describes an evidence-based method guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to systematically search, select, and appraise apps targeting phonics knowledge and phonological awareness skills, present in Google Play and the App Store. We intend to perform a systematic and comprehensive search and a 2-step process for screening: (1) broad screening (app titles) and (2) narrow screening (marketing descriptions). Quality appraisal of the included apps will involve two structured appraisal tools: (1) the Mobile Application Rating Scale and (2) the Appraising Apps for Reading Checklist. RESULTS: This method will help determine the number of apps targeting phonics knowledge and phonological awareness, present on the Android and iOS platforms. The content, quality, and usability of these apps will be determined using structured appraisal tools. We have planned to conduct searches on Google Play and the App Store in January-March 2021; broad and focused screening, from April 2021; and data extraction and quality appraisal in October 2021. CONCLUSIONS: This protocol provides a basis for locating and evaluating apps targeting phonics knowledge and phonological awareness skills. This protocol will support practitioners, educators, and families to make informed decisions when purchasing apps for instructional use. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/23921.

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)0.111
metaresearch head score (Gemma)0.170
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.111
Threshold uncertainty score0.586

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1110.170
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0110.012
Bibliometrics0.0090.010
Science and technology studies0.0050.006
Scholarly communication0.0070.007
Open science0.0040.005
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0640.012

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 head0.792
GPT teacher head0.717
Teacher spread0.075 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations5
Published2021
Admission routes1
Has abstractyes

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