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Examining Structural and Clinical Factors Associated with Implementation of Standing Orders for Adult Immunization

2011· article· en· W2087423027 on OpenAlexaff
Michael Yonas, Mary Patricia Nowalk, Richard K. Zimmerman, Faruque Ahmed, Steven M. Albert

Bibliographic record

VenueJournal for Healthcare Quality · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsIntertek (Canada)
FundersCenters for Disease Control and Prevention
KeywordsFamily medicineMedicineFocus groupNursingHealth careQualitative research

Abstract

fetched live from OpenAlex

A proven method to increase vaccination rates in primary care is a standing orders program (SOP) for nonphysician staff to assess and vaccinate eligible individuals without a specific written physician order. This study describes a mixed methods approach to examining physicians' beliefs and attitudes about and adoption of SOPs for adult immunizations, specifically, influenza and pneumococcal polysaccharide vaccine. Focus groups and in-depth interviews of physicians, nurses, practice managers, and the medical director of a managed care health plan were conducted. Results were used to enrich a concise survey based on the Awareness-to-Adherence model of physician behavior and previous research, which was mailed to 1,640 general internists and family physicians nationwide. Barriers to SOPs identified through qualitative methods were lack of interest in changing the status quo, a physician-dominated hierarchy, and fear of malpractice. Facilitators included having an electronic medical record and a practice culture that was open to change. The survey (response rate 67%) confirmed the facilitators and further identified patient, physician, and practice factors that served as barriers to establishing and maintaining SOPs. This mixed methods approach provided the opportunity to develop a tailored and practice-oriented survey for examining the contextual factors influencing clinical providers' decisions to implement SOPs for adult immunization.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.078
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.356
GPT teacher head0.518
Teacher spread0.161 · 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

Citations15
Published2011
Admission routes1
Has abstractyes

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