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Record W2097873474 · doi:10.1007/s11999-015-4325-7

Orthopaedic Trainees Retain Knowledge After a Partner Abuse Course: An Education Study

2015· article· en· W2097873474 on OpenAlexafffund
Kim Madden, Sheila Sprague, Brad Petrisor, Forough Farrokhyar, Michelle Ghert, Marium Kirmani, Mohit Bhandari

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsMcMaster University
FundersMcMaster UniversityCanadian Institutes of Health ResearchHamilton Health Sciences
KeywordsMedicineCurriculumDomestic violenceTest (biology)Suicide preventionPoison controlFamily medicineInjury preventionOccupational safety and healthHuman factors and ergonomicsSports medicineMedical educationMedical emergencyPsychiatryPsychology

Abstract

fetched live from OpenAlex

INTRODUCTION: Intimate partner violence (IPV) is a serious global issue that results in a large number of injuries and deaths among women. Educating clinicians about IPV can help providers identify, prevent, and treat victims, and, ultimately, improve care for victims of abuse. We sought to determine the effect of a half-day educational course on IPV for orthopaedic surgical trainees on knowledge and attitudes. QUESTIONS/PURPOSES: We asked (1) whether a half-day educational course on IPV can improve orthopaedic surgical trainees' knowledge and (2) attitudes regarding IPV; and (3) whether a course on IPV can be accepted and viewed as valuable by trainees? METHODS: Using published research on IPV in patients with musculoskeletal injuries, we developed a half-day educational course. The curriculum included lectures and discussion regarding the basics of IPV, the current state of IPV research, what to do when a patient is a victim or perpetrator, and the orthopaedic surgeon's role in recognizing, preventing, and assisting with IPV. All 33 course participants (30 men and three women), all orthopaedic surgical trainees, completed a questionnaire that included general true or false or agree or disagree statements regarding their knowledge, attitudes, and practices of IPV in the musculoskeletal setting; the questionnaire also included a knowledge test of 25 true or false statements. The questionnaire was administered immediately before, immediately after, and 3 months after the course; 76% (25 of 33) took the test immediately after the course and 82% (27 of 33) completed the test at 3 months. Participant knowledge scores were compared across the three different times to determine the effect of the course. RESULTS: Participants increased their knowledge after the course, and the increased knowledge was retained at retesting at 3 months; the mean percentage of correct answers before the course was 57%, which increased to 73% after the course, and was 68% 3 months later (F = 9.505; p = 0.001). Before the course, most of the course participants (30 of 32; 94%) agreed that IPV is an important issue; agreement increased to 100% immediately after the course. The largest change in attitude was in response to the statement: "I am skeptical that the health care system has the resources to screen for IPV." Before the course, 53% (17 of 32) of trainees endorsed this statement, but the percent decreased to 36% (nine of 25) after the course and remained low at 33% (nine of 27), at the 3-month test. CONCLUSIONS: Our findings show that a short course on IPV in patients with musculoskeletal injuries led to an improvement and retention of knowledge 3 months after the course. Based on our findings, we recommend that IPV education be integrated in training programs for orthopaedic surgeons. Future projects should focus on developing and implementing a sustainable education program that can affect practice for healthcare professionals and trainees in multiple clinical settings.

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)0.004
metaresearch head score (Gemma)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.195
GPT teacher head0.538
Teacher spread0.343 · 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

Citations17
Published2015
Admission routes2
Has abstractyes

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