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Record W2738016224 · doi:10.1503/cjs.002316

The Orthopedic Trauma Symposium: improving care of orthopedic injuries in Haiti

2017· article· en· W2738016224 on OpenAlexaffvenue
Ryan Normore, H. Michelle Greene, Allison DeLong, Andrew Furey

Bibliographic record

VenueCanadian Journal of Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineLikert scaleMedical educationPsychological interventionNursingPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Although single-trip volunteer medical teams can provide much-needed acute trauma care following natural disasters, their ability to leave a legacy of improved care in the region is often limited. One way to improve treatment of traumatic injuries is through conference-based teaching, such as the Orthopedic Trauma Symposium (OTS), which took place in Haiti in 2014. However, there is little research evaluating the effectiveness of such teaching tools. We evaluated the OTS and the potential benefits of future iterations of the course. METHODS: A survey consisting of 5-point Likert scale questions as well as qualitative open feedback assessed respondents' opinions regarding the value, content and delivery of the OTS. Respondents were classified dichotomously in terms of their role in the OTS (instructor v. participant) to measure any meaningful difference in feedback. RESULTS: In total, 84% of all participants agreed that course content was clearly communicated, and 98% agreed that instructors were knowledgeable in the topics covered. Moreover, 87% of all participants responded that they would apply the training in their medical practices going forward. CONCLUSION: Haitian physicians, residents and medical students responded favourably to the OTS. Open-ended questions offered concise, attainable improvements for future iterations of the course. Organizations committed to improving medical care in low- and middle-income countries should take note of these findings while continuing to develop the OTS and similar initiatives globally.

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)0.002
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 score0.117
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.277
Teacher spread0.252 · 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

Citations5
Published2017
Admission routes2
Has abstractyes

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