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Record W4414519901 · doi:10.2196/84712

Questionnaires Used to Explore the Perspectives of Parents and Health Professionals on Young Children’s Use of Technology: Systematic Review

2025· article· en· W4414519901 on OpenAlexvenueno aff
Charlotte Lund Rasmussen, Ivan P.H. Au, Danica Hendry, Amber Beynon, Sarah Stearne, George Thomas, Kate Mannell, Lisa Kervin, Susan Edwards, Courtenay Harris, Leon Straker, Juliana Zabatiero

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisHealth professionalsFocus groupQuality (philosophy)Data extractionDigital healthHealth careMEDLINE

Abstract

fetched live from OpenAlex

Abstract Background Technology is integrated into many children’s daily lives, with parents’ and health professionals’ perspectives shaping children’s technology use. Measuring and understanding these perspectives are essential for developing strategies for supporting adults in decision-making that help children thrive in a digital world. Objective This systematic review aimed to investigate the psychometric properties of questionnaires used to assess parents’ and health professionals’ perspectives on young children’s use of technology related to health, well-being, and development. The secondary aim was to synthesize findings on these perspectives. Methods Peer-reviewed papers published between January 2010 and September 2024 were identified through searches in 7 electronic databases. Studies were included if they examined parental or health care professionals’ perspectives on technology use among children aged birth to 5 years. Two reviewers (CLR and IPHA) independently conducted the data extraction and study quality assessment. Reported psychometric properties of the questionnaires were synthesized. Deductive thematic analysis was used to explore the content focus of the questionnaire used in the included studies and synthesize the reported perspectives. Results In total, 85 studies were included, all involving parents. No study investigated health professionals’ perspectives. The methodological quality of the studies was generally low, with 62 studies scoring below the threshold for acceptable quality. In total, 52 studies reported psychometric properties of the questionnaires used, of which, only 15 studies reported more than 1 measure of validity or reliability. A total of 75 studies reported participants’ perspectives on children’s technology use. Findings revealed that parents generally supported the role of digital devices in enhancing learning but expressed concerns about potential negative impacts on children’s physical health, emotions, and behaviors. Conclusions Parents’ perspectives on children’s technology use were frequently assessed through questionnaires, though the validity of these questionnaires was often poor, with limited psychometric testing. Parental perspectives were mixed with educational benefits being recognized, while countered with concerns about the impact on children’s physical health and development. High-quality questionnaires are needed to generate stronger evidence informing strategies to support families in technology use decision-making with and for children.

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)0.049
metaresearch head score (Gemma)0.173
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: Review · Consensus signal: Review
Teacher disagreement score0.049
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.173
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0100.011
Bibliometrics0.0270.025
Science and technology studies0.0010.002
Scholarly communication0.0040.007
Open science0.0030.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.056
GPT teacher head0.372
Teacher spread0.315 · 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
GenreReview

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

Citations1
Published2025
Admission routes1
Has abstractyes

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