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Record W3175284670 · doi:10.2196/27504

Impact of Digital Educational Interventions to Support Parents Caring for Acutely Ill Children at Home and Factors That Affect Their Use: Protocol for a Systematic Review

2021· review· en· W3175284670 on OpenAlexvenueno aff
Madison Milne‐Ives, Sarah Neill, Natasha Bayes, Mitch Blair, Jane Blewitt, Lucy Bray, Enitan D. Carrol, Bernie Carter, Rob Dawson, Paul Dimitri, Monica Lakhanpaul, Damian Roland, Alison Tavaré, Edward Meinert

Bibliographic record

VenueJMIR Research Protocols · 2021
Typereview
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsnot available
FundersOxford Academic Health Science NetworkGreat Ormond Street Institute of Child HealthMeningitis Research FoundationNational Institute for Health and Care Research
KeywordsCINAHLPsychological interventionUsabilitySystematic reviewMedicineProtocol (science)MEDLINECochrane LibraryPopulationIntervention (counseling)Inclusion (mineral)Health careHealth literacyNursingPsychologyFamily medicineAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Urgent and emergency care health services are overburdened, and the use of these services by acutely ill infants and children is increasing. A large proportion of these visits could be sufficiently addressed by other health care professionals. Uncertainty about the severity of a child's symptoms is one of many factors that play a role in parents' decisions to take their children to emergency services, demonstrating the need for improved support for health literacy. Digital interventions are a potential tool to improve parents' knowledge, confidence, and self-efficacy at managing acute childhood illness. However, existing systematic reviews related to this topic need to be updated and expanded to provide a contemporary review of the impact, usability, and limitations of these solutions. OBJECTIVE: The purpose of this systematic review protocol is to present the method for an evaluation of the impact, usability, and limitations of different types of digital educational interventions to support parents caring for acutely ill children at home. METHODS: The review will be structured using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) and Population, Intervention, Comparator, and Outcome (PICO) frameworks. Five databases will be systematically searched for studies published in English during and after 2014: Medline, EMBASE, CINAHL, APA PsycNet, and Web of Science. Two reviewers will independently screen references' titles and abstracts, select studies for inclusion based on the eligibility criteria, and extract the data into a standardized form. Any disagreements will be discussed and resolved by a third reviewer if necessary. Risk of bias of all studies will be assessed using the Mixed-Methods Appraisal Tool (MMAT), and a descriptive analysis will be used to evaluate the outcomes reported. RESULTS: The systematic review will commence during 2021. CONCLUSIONS: This systematic review will summarize the impact, usability, and limitations of digital interventions for parents with acutely ill children. It will provide an overview of the field; identify reported impacts on health and behavioral outcomes as well as parental knowledge, satisfaction, and decision making; and identify the factors that affect use to help inform the development of more effective and sustainable interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/27504.

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)0.094
metaresearch head score (Gemma)0.112
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.094
Threshold uncertainty score0.499

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0940.112
Meta-epidemiology (narrow)0.0070.007
Meta-epidemiology (broad)0.0250.024
Bibliometrics0.0150.014
Science and technology studies0.0060.006
Scholarly communication0.0090.010
Open science0.0060.007
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0570.007

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 head0.730
GPT teacher head0.690
Teacher spread0.041 · 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 designSystematic review
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

Citations3
Published2021
Admission routes1
Has abstractyes

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