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Record W4244758399 · doi:10.2196/11963

A Web-Based Alcohol Screening and Brief Intervention Training Module Within Physician Assistant Programs in the Midwest to Increase Knowledge, Attitudes, and Confidence: Evaluation Study

2019· article· en· W4244758399 on OpenAlexvenueno aff
Leigh E Tenkku Lepper, Tracy Cleveland, Genevieve DelRosario, Katherine Ervie, Catherine Link, Lara Oakley, Abdelmoneim Elfagir, Debra J Sprague

Bibliographic record

VenueJMIR Mental Health · 2019
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersSubstance Abuse and Mental Health Services Administration
KeywordsCurriculumMedical educationIntervention (counseling)MedicineWeb applicationBrief interventionHealth carePsychologyNursingComputer scienceWorld Wide WebPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Preventing and reducing risky alcohol use and its side effects remains a public health priority. Discussing alcohol use with patients can be difficult; dedicated training for health care providers is needed to facilitate these conversations. A Web-based alcohol screening and brief intervention (SBI), comprising didactic and skills application training, was designed for physician assistant students. OBJECTIVE: This paper details experiences and outcomes in developing an alcohol SBI training curriculum and coordinating virtual encounters with standardized patients. We also explain challenges faced with developing an alcohol SBI training and a Web-based learning management site to fit the needs of 5 different physician assistant programs. METHODS: Training development comprised 3 phases-precourse, development, and implementation. The precourse phase included developing the initial training curriculum, building a website, and testing with a pilot group. The development phase refined the training curriculum based on user feedback and moved into a three-component module: didactic training module, guided interactive encounter with a simulated patient, and live encounter with a standardized patient. A learning management system website was also created. In the implementation phase, 5 physician assistant schools incorporated the Web-based training into curricula. Each school modified the implementation method to suit their organizational environment. Evaluation methods included pre- and postchange over time on trainee attitudes, knowledge, and skills (confidence) on talking to patients about alcohol use, trainee self-reported proficiency on the standardized patient encounter, standardized patient evaluation of the trainee proficiency during the alcohol use conversation, user evaluation of the type of technology mode for the standardized patient conversation, and overall trainee satisfaction with the Web-based training on alcohol SBI. RESULTS: Final evaluation outcomes indicated a significant (P<.01) change over time in trainee knowledge and skills (confidence) in the conduct of the alcohol SBI with a standardized patient, regardless of the program implementation method. Trainees were generally satisfied with the Web-based training experience and rated the use of the videoconference medium as most useful when conducting the alcohol SBI conversation with the standardized patient. Training that included a primer on the importance of screening, individual participation in the Web-based didactic alcohol SBI modules, and virtual encounters with standardized patients through a university-based simulation center was the most widely accepted. Successful implementation included program investment and curriculum planning. Implementation barriers involved technical challenges with standardized patient encounters and simulation center logistics, and varying physician assistant school characteristics. CONCLUSIONS: Development and implementation of Web-based educational modules to educate health care professionals on alcohol SBI is effective, easy to reproduce, and readily accessible. Identifying challenges affecting development, implementation, and utilization of learned techniques in practice, enhances facilitation of learning and training efficacy. As the value of technology-based learning becomes more apparent, reports detailing what has worked versus what has not may help guide the process.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.086
GPT teacher head0.407
Teacher spread0.321 · 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 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

Citations4
Published2019
Admission routes1
Has abstractyes

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