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Record W2738141572 · doi:10.1097/adm.0000000000000340

Screening Adolescents for Alcohol Use: Tracking Practice Trends of Massachusetts Pediatricians

2017· article· en· W2738141572 on OpenAlexaff
Sharon Levy, Rosemary E. Ziemnik, Sion Kim Harris, Lily Rabinow, Leigh Breen, Christina Fluet, Heather Strother, John H. Straus

Bibliographic record

VenueJournal of Addiction Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsMedicineTracking (education)AlcoholFamily medicinePsychiatryPsychology

Abstract

fetched live from OpenAlex

OBJECTIVES: Substance use screening is a recommended component of routine healthcare for adolescents. A 2008 survey of Massachusetts primary care physicians found high rates of screening, but low rates of validated tool use, leading to the concern that physicians may be missing substance use-related problems and disorders. In an effort to improve practice, a cross-disciplinary group developed and distributed an adolescent screening, brief intervention, and referral to treatment toolkit in 2009. A new survey of Massachusetts primary care physicians was conducted in 2014; this report describes its findings, and compares them to those from 2008. METHODS: A survey was mailed to a randomly selected sample of Massachusetts primary care physicians listed in the state Board of Registration in Medicine database. Item response frequencies were computed. Multiple logistic regression modeling was used to compare 2008 and 2014 responses, while controlling for any demographic differences between samples. RESULTS: Pediatrician respondents in 2014 (analysis N = 130) reported a high rate of annually screening patients for alcohol use (96.2%), but only 56.2% reported using a validated screening tool. Rates of screening and validated tool use were higher in 2014 than 2008. Insufficient knowledge as a reported barrier to screening decreased from 2008 to 2014. However, lack of time or staff resources remained key perceived barriers to screening. CONCLUSIONS: Our findings suggest that adolescent alcohol use screening practices among Massachusetts pediatricians have improved in recent years, during a time of national and statewide efforts to educate physicians. However, opportunities for practice improvement remain.

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)0.003
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 score0.126
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.081
GPT teacher head0.375
Teacher spread0.294 · 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

Citations27
Published2017
Admission routes1
Has abstractyes

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