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Record W3185315835 · doi:10.2196/27551

Engaging Parents and Health Care Stakeholders to Inform Development of a Behavioral Intervention Technology to Promote Pediatric Behavioral Health: Mixed Methods Study

2021· article· en· W3185315835 on OpenAlexvenueno aff
Sean M. O’Dell, Heidi R Fisher, Victoria Schlieder, Tracey Klinger, Rachel Kininger, McKenna Cosottile, Stacey Cummings, Kathy DeHart

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2021
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosocialPsychological interventionIntervention (counseling)Health carePsychologyFocus groupMedicineNursingApplied psychologyPsychiatryBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: Despite effective psychosocial interventions, gaps in access to care persist for youth and families in need. Behavioral intervention technologies (BITs) that apply psychosocial intervention strategies using technological features represent a modality for targeted prevention that is promising for the transformation of primary care behavioral health by empowering parents to take charge of the behavioral health care of their children. To realize the potential of BITs for parents, research is needed to understand the status quo of parental self-help and parent-provider collaboration to address behavioral health challenges and unmet parental needs that could be addressed by BITs. OBJECTIVE: The aim of this study is to conduct foundational research with parents and health care stakeholders (HCS) to discover current practices and unmet needs related to common behavioral health challenges to inform the design, build, and testing of BITs to address these care gaps within a predominantly rural health system. METHODS: We conducted a convergent mixed-parallel study within a large, predominantly rural health system in which the BITs will be developed and implemented. We analyzed data from parent surveys (N=385) on current practices and preferences related to behavioral health topics to be addressed in BITs along with focus group data of 48 HCS in 9 clinics regarding internal and external contextual factors contributing to unmet parental needs and current practices. By comparing and relating the findings, we formed interpretations that will inform subsequent BIT development activities. RESULTS: Parents frequently endorsed several behavioral health topics, and several topics were relatively more or less frequently endorsed based on the child's age. The HCS suggested that BITs may connect families with evidence-based guidance sooner and indicated that a web-based platform aligns with how parents already seek behavioral health guidance. Areas of divergence between parents and HCS were related to internalizing problems and cross-cutting issues such as parenting stress, which may be more difficult for health care HCS to detect or address because of the time constraints of routine medical visits. CONCLUSIONS: These findings provide a rich understanding of the complexity involved in meeting parents' needs for behavioral health guidance in a primary care setting using BITs. User testing studies for BIT prototypes are needed to successfully design, build, and test effective BITs to empower parents to take charge of promoting the behavioral health of their 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)0.045
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.167
GPT teacher head0.507
Teacher spread0.340 · 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 designQualitative
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

Citations6
Published2021
Admission routes1
Has abstractyes

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