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Record W2313302498 · doi:10.1097/acm.0000000000001122

Hurdles to Legislative Advocacy Training in the United States

2016· letter· en· W2313302498 on OpenAlexaboutno aff
Evan G. Pivalizza, Omonele O. Nwokolo, George Washington Williams

Bibliographic record

VenueAcademic Medicine · 2016
Typeletter
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsLegislatureMandateState legislaturePolitical scienceLegislatorState (computer science)AccreditationCurriculumPleaPublic administrationPublic relationsMedicineLawLegislation

Abstract

fetched live from OpenAlex

To the Editor: We read the plea of Bhate and Loh1 for mandatory advocacy training in Canada with great interest. We echo the call for medical student/resident education in health advocacy and describe our own initiatives in a U.S. state-sponsored institution. The Accreditation Council for Graduate Medical Education requires legislative advocacy education. For example, program requirements for anesthesiology residency2(p19) indicate the program’s responsibility to educate residents about “legislative and regulatory issues.” We accept this charge and update residents and medical students by conference or lunch meetings of legislative and regulatory issues germane to their future careers. However, as physicians at a state institution, we are subject to a number of draconian regulations that create barriers to effective legislative advocacy education. First, administrative staff may be uncomfortable sending electronic reminders or nonpolitical announcements about educational events in fear of state policy which prohibits use of state “resources” for legislative advocacy. In accordance with policy, these electronic announcements must be apolitical, must not be interpreted as favoring a candidate/legislator unless physicians need to contact them about an impending issue, and must not be coercive. Second, the state medical association and their council on medical education are reluctant to support resolutions to encourage or mandate legislative advocacy educational efforts, preferring not to get involved in specific school curricula. This self-imposed paralysis at the administrative level prevents effective, appropriate legislative medical education and disadvantages physicians while nonphysicians are afforded such opportunities in their training.3 State and national specialty societies have an opportunity to offer innovative solutions to these challenges. The Texas Society of Anesthesiologists encourages and facilitates resident participation in governmental committees, sponsors CPR training events for legislative staff, and interactive legislative educational events for residents. They have instituted a competition between state anesthesia departments to encourage resident participation in the nonpartisan American Society of Anesthesiologists Political Action Committee. Participation is educational in nature as a nominal $25 contribution is not primarily economic. Even in these instances, residents are potentially limited by state regulations and are frequently forced to communicate “off-line” and on nonuniversity sites. We thank Bhate and Loh for the call to action. As they note, Canadian students are already seeking independent opportunities to achieve some of these educational goals, and we risk a similar fate if we cannot provide U.S. students with the legislative advocacy education they need. Evan G. Pivalizza, MD Professor of anesthesiology, University of Texas Medical School–Houston, Houston, Texas; [email protected] Omonele O. Nwokolo, MD Assistant professor of anesthesiology, University of Texas Medical School–Houston, Houston, Texas. George W. Williams, MD Assistant professor of anesthesiology, University of Texas Medical School–Houston, Houston, Texas.

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)0.018
metaresearch head score (Gemma)0.079
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.031
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.079
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.006
Scholarly communication0.0080.008
Open science0.0040.003
Research integrity0.0310.042
Insufficient payload (model declined to judge)0.0060.002

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 head0.096
GPT teacher head0.382
Teacher spread0.286 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations1
Published2016
Admission routes1
Has abstractyes

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