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Record W4392285488 · doi:10.1177/00099228241234229

Feasibility and Acceptability of a Pediatric Primary Care Physician Training for Anxiety Screening and Evidence-based Intervention

2024· article· en· W4392285488 on OpenAlexaff
Julie A. Wojtaszek, Hannah L. Ham, Teryn Bruni, Eleah Sunde, Claudia Drossel, Alexandros Maragakis

Bibliographic record

VenueClinical Pediatrics · 2024
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsAlgoma University
FundersHealth Resources and Services Administration
KeywordsMedicineAnxietyPsychological interventionIntervention (counseling)PopulationPrimary careFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

Anxiety is one of the most prevalent psychological conditions in the pediatric population, and its associated impairments often persist into adulthood. Pediatricians are in a unique position to screen, briefly intervene, and facilitate treatment to prevent long-term impacts. However, they often do not have adequate training to do so. The current study addressed this gap by providing a brief online educational workshop aimed to promote: (1) screening for anxiety and (2) follow-up with appropriate evidence-based interventions. Fifty-three providers participated, and 38 completed surveys pre- and post-training. Findings indicate acceptability of the training to providers, improved knowledge related to anxiety, and increased readiness to manage anxiety during a medical visit. This study supports the utility of a brief, online training on screening and provision of evidence-based treatment for anxiety in pediatric primary care.

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)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.040
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
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 head0.179
GPT teacher head0.419
Teacher spread0.240 · 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 designNon-randomized trial
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

Citations0
Published2024
Admission routes1
Has abstractyes

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