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Record W4224557614 · doi:10.2147/oaem.s341709

Emergency Medicine Physicians’ Views on Providing Unnecessary Management in the Emergency Department

2022· article· en· W4224557614 on OpenAlexaff
Abdulaziz Alalshaikh, Bader Alyahya, Mohammed Almohawes, Mosaed Alnowiser, Mohammed Ghandour, Mohammed Alyousef, Fahad Abuguyan, Abdulaziz Almehlisi, Fawaz Altuwaijri, Mohammed K Alageel

Bibliographic record

VenueOpen Access Emergency Medicine · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsUniversity of British Columbia
FundersCollege of Medicine, King Saud UniversityKing Saud UniversityABIM Foundation
KeywordsMedicineEmergency departmentPsychological interventionMalpracticeMedical emergencyDefensive medicineFamily medicineCross-sectional studyEmergency medicineMedical malpracticeNursing

Abstract

fetched live from OpenAlex

Purpose: To assess the views of emergency medicine physicians (EMPs) on the practice of providing unnecessary medical management in the emergency department. Methods: All EMPs in Saudi Arabia were approached to participate in this cross-sectional study. A self-administered online survey that collected the participants' demographic information and opinions regarding the unnecessary management provided by EMPs in Saudi Arabia was conducted between December 2020 and February 2021. SPSS 22.0 was used to analyze the data. Results: A total of 181 EMPs returned the questionnaire. More than 80% of the participants believed that EMPs order unnecessary tests or procedures at least a few times per week. The major reasons for ordering unnecessary medical tests or procedures were "concern about malpractice issues" (60.8%), "not having enough time with a patient for meaningful discussion" (47%), and "just to be safe" (46.4%). More than 55% of the respondents also believed that EMPs are in the best position to address the problem of unnecessary testing. Conclusion: Most of the EMPs who participated in this study recognized that ordering unnecessary tests is a serious problem that happens on a daily basis. Many factors and reasons were described by the participants, and multiple possible solutions were suggested to help overcome the issue. Evaluating physicians' perspectives on the issue is a key step in addressing the problem and implementing appropriate interventions.

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)0.019
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.706
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0040.000
Scholarly communication0.0000.002
Open science0.0070.004
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.1560.000

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 head0.747
GPT teacher head0.637
Teacher spread0.110 · 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.

Study designNot applicable
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

Citations8
Published2022
Admission routes1
Has abstractyes

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