MétaCan
Menu
Back to cohort
Record W4221050680 · doi:10.2196/30795

Online-Delivered Over Staff-Delivered Parenting Intervention for Young Children With Disruptive Behavior Problems: Cost-Minimization Analysis

2022· article· en· W4221050680 on OpenAlexvenueno aff
Justin B. Ingels, Phaedra S. Corso, Ronald J. Prinz, Carol W. Metzler, Matthew R. Sanders

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2022
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
FundersNational Institute of Mental HealthNational Institutes of Health
KeywordsPsychological interventionIntervention (counseling)Context (archaeology)MedicinePopulationRandomized controlled trialMental healthFamily medicineClinical psychologyPsychologyNursingPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: High-prevalence childhood mental health problems like early-onset disruptive behavior problems (DBPs) pose a significant public health challenge and necessitate interventions with adequate population reach. The treatment approach of choice for childhood DBPs, namely evidence-based parenting intervention, has not been sufficiently disseminated when relying solely on staff-delivered services. Online-delivered parenting intervention is a promising strategy, but the cost minimization of this delivery model for reducing child DBPs is unknown compared with the more traditional staff-delivered modality. OBJECTIVE: This study aimed to examine the cost-minimization of an online parenting intervention for childhood disruptive behavior problems compared with the staff-delivered version of the same content. This objective, pursued in the context of a randomized trial, made use of cost data collected from parents and service providers. METHODS: A cost-minimization analysis (CMA) was conducted comparing the online and staff-delivered parenting interventions. Families (N=334) with children 3-7 years old, who exhibited clinically elevated disruptive behavior problems, were randomly assigned to the two parenting interventions. Participants, delivery staff, and administrators provided data for the CMA concerning family participation time and expenses, program delivery time (direct and nondirect), and nonpersonnel resources (eg, space, materials, and access fee). The CMA was conducted using both intent-to-treat and per-protocol analytic approaches. RESULTS: =19.1; P<.001) compared to the staff-delivered intervention. The mean incremental cost difference between the interventions was $1164 total costs per case. The same pattern of significant differences was confirmed in the per-protocol analysis based on the families who completed their respective intervention, with a mean incremental cost difference of $1483 per case. All costs were valued or adjusted in 2017 US dollars. CONCLUSIONS: The online-delivered parenting intervention in this randomized study produced substantial cost minimization compared with the staff-delivered intervention providing the same content. Cost minimization was driven primarily by personnel time and, to a lesser extent, by facilities costs and family travel time. The CMA was accomplished with three critical conditions in place: (1) the two intervention delivery modalities (ie, online and staff) held intervention content constant; (2) families were randomized to the two parenting interventions; and (3) the online-delivered intervention was previously confirmed to be non-inferior to the staff-delivered intervention in significantly reducing the primary outcome, child disruptive behavior problems. Given those conditions, cost minimization for the online parenting intervention was unequivocal. TRIAL REGISTRATION: ClinicalTrials.gov NCT02121431; https://clinicaltrials.gov/ct2/show/NCT02121431.

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)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.007
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.000

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 head0.022
GPT teacher head0.303
Teacher spread0.281 · 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 designSimulation or modeling
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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Pediatrics and ParentingSame topicChild and Adolescent Psychosocial and Emotional DevelopmentFrench-language works237,207