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Evaluating the Effect of a Cadaver‐Based Video Resource on the Pelvic Binding Competencies of Firefighters

2019· article· en· W3176488757 on OpenAlexaff
Ryan Alexander Medhurst, William Albabish, Alexander L. Stubbs, Naomi Robson, Jim Petrik, Lorraine Jadeski

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPelvisMedicineCadaverCadaveric spasmPelvic fractureIntervention (counseling)Physical therapyRadiologySurgeryNursing

Abstract

fetched live from OpenAlex

A pelvic fracture is a life‐threatening injury that requires accurate pre‐hospital care. Pelvic binding is an effective, non‐invasive procedure that can manage haemorrhages associated with most pelvic fractures. However, pelvic binding is a high precision skill that requires proper training to ensure proficiency. Previous studies have shown that pelvic binding is both irregularly and inaccurately performed at several tiers of emergency medicine. One plausible explanation for this competency issue is that training associated with pelvic binding is often brief and does not clearly explain the ‘why’ behind the procedure. Using a compilation of cadaveric images that emphasized the important anatomy related to pelvic binding, a cadaver‐based video resource was created to supplement traditional teaching of this skill. The present study examined the effect of this cadaver‐based video resource on the pelvic binding competencies of emergency responders, specifically firefighters. The study used a double‐blinded approach – participants (n = 16) were sorted into two groups that were balanced according to their previous first‐aid experience. The control group was given 20 minutes to practice the skill of pelvic binding, while the intervention group was given a three‐minute cadaver‐based video resource that focused on the anatomy of the pelvis and pelvic binding, followed by 17 minutes to practice the skill of pelvic binding. Over three visits, participants performed three written and three clinical competency tests to assess their pelvic binding knowledge and their ability to apply a pelvic binder. The first set of tests were administered one week prior to the intervention to record baseline competency. The second set of tests were administered post‐intervention to measure any changes in scores. The final set of tests were administered three weeks after the intervention to assess knowledge retention. The primary outcome measures evaluated criteria related to the proper placement of the pelvic binder. The written tests assessed whether the participant knew the location at which the pelvic binder should be applied, while the clinical competency tests assessed whether the participant could translate that knowledge and successfully apply the pelvic binder to a subject. Preliminary results showed a positive trend in both knowledge of pelvic binding and performance of the skill favouring the group who had access to the video resource. According to Fisher's Exact test, a significant difference in scores associated with pelvic binding accuracy was observed (p=0.026) between the control and intervention groups. The intervention group identified the correct landmark for binder placement with 100% accuracy, while the control group correctly identified the landmark only 37.5% of the time. These findings suggest that targeted cadaver‐based training videos may be valuable tools for the development of clinical competencies. Overall, this study may offer insights into the development of cadaver‐based educational resources to supplement training protocols and enhance the understanding of emergency responders. By explaining the anatomy involved in emergency procedures, the ‘why’ behind clinical protocols can be clarified. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.094
GPT teacher head0.432
Teacher spread0.338 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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