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Record W4391102261 · doi:10.1037/fsh0000867

Children’s behavioral and mental health in primary care settings: A survey of self-reported comfort levels and practice patterns among pediatricians.

2024· article· en· W4391102261 on OpenAlexaff
Anne E. Brisendine, Susan Griffin, Jane Duer

Bibliographic record

VenueFamilies Systems & Health · 2024
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsEducation and Early Childhood Development
FundersU.S. Department of Health and Human Services
KeywordsMental healthPsycINFOWorkforceSpecialtyPsychological interventionPsychologyMedicineNursingMEDLINEFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Despite the well-documented youth mental health crisis, there has been a lag in the development of a specialized workforce to meet needs of young people experiencing these challenges. Little is known about the comfort of primary care pediatricians when faced with children and adolescents with mental health care concerns. METHOD: A brief online survey was conducted to assess patterns of behavioral and mental health concerns in pediatric practices affiliated with a pediatric health system in Alabama. The survey asked about frequency of conditions that providers encountered, comfort treating these conditions, and frequency of external referrals. RESULTS: Pediatric providers reported high volumes of children with mental health concerns and varying levels of comfort treating independently. Providers frequently refer externally. CONCLUSIONS: High rates of referrals could further stress an already overloaded system of specialty care. Interventions must be implemented to ensure a workforce prepared to meet the growing needs of youth requiring support for mental and behavioral health conditions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.052
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.329
Teacher spread0.301 · 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 teacher head, 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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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