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Record W3102262972 · doi:10.2196/15149

Health Personnel’s Perceived Usefulness of Internet-Based Interventions for Parents of Children Younger Than 5 Years: Cross-Sectional Web-Based Survey Study

2020· article· en· W3102262972 on OpenAlexvenueno aff
Hege Therese Størksen, Silje Marie Haga, Kari Slinning, Filip Drozd

Bibliographic record

VenueJMIR Mental Health · 2020
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionMental healthThe InternetMedicineSocioemotional selectivity theoryPsychologyNursingFamily medicinePsychiatryGerontology

Abstract

fetched live from OpenAlex

BACKGROUND: Approximately 10%-15% of children struggle with different socioemotional and psychological difficulties in infancy and early childhood. Thus, health service providers should have access to mental health interventions that can reach more parents than traditional face-to-face interventions. However, despite increasing evidence on the efficacy of internet-based mental health interventions, the pace in transferring such interventions to health care has been slow. One of the major suggested barriers to this may be the health personnel's attitudes to perceived usefulness of internet-based interventions. OBJECTIVE: The purpose of this study was to examine health professionals' perceived usefulness of internet-based mental health interventions and to identify the key areas that they consider new internet-based services to be useful. METHODS: Between May and September 2018, 2884 leaders and practitioners of infant and child health services were recruited to a cross-sectional web-based survey through the following channels: (1) existing email addresses from the Regional Centre for Child and Adolescent Mental Health, Eastern and Southern Norway, course database, (2) an official mailing list to infant and child health services, (3) social media, or (4) other recruitment channels. Respondents filled in background information and were asked to rate the usefulness of internet-based interventions for 12 different infant and child mental health problem areas based on the broad categories from the Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood (DC:0-5). Perceived usefulness was assessed with 1 global item: "How often do you think internet-based self-help programs can be useful for following infant and child mental health problems in your line of work?" The answers were scored on a 4-point scale ranging from 0 (never) to 3 (often). RESULTS: The participants reported that they sometimes or often perceived internet-based interventions as useful for different infant and child mental health problems (scale of 0-3, all means>1.61). Usefulness of internet-based interventions was rated acceptable for sleep problems (mean 2.22), anxiety (mean 2.09), and social withdrawal and shyness (mean 2.07), whereas internet-based interventions were rated as less useful for psychiatric problems such as obsessive behaviors (mean 1.89), developmental disorders (mean 1.91), or trauma (mean 1.61). Further, there were a few but small differences in perceived usefulness between service leaders and practitioners (all effect sizes<0.32, all P<.02) and small-to-moderate differences among daycare centers, well-baby clinics, municipal child welfare services, and child and adolescent mental health clinics (all effect sizes<0.69, all P<.006). CONCLUSIONS: Internet-based interventions for different infant and child mental health problems within services such as daycare centers, well-baby clinics, municipal child welfare services, and child and adolescent mental health clinics are sometimes or often perceived as useful. These encouraging findings can support the continued exploration of internet-based mental health interventions as a way to improve parental support.

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)0.003
metaresearch head score (Gemma)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.121
GPT teacher head0.445
Teacher spread0.325 · 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".

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Citations12
Published2020
Admission routes1
Has abstractyes

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