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Enregistrement W3176488757 · doi:10.1096/fasebj.2019.33.1_supplement.203.2

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

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

Notice bibliographique

RevueThe FASEB Journal · 2019
Typearticle
Langueen
DomaineHealth Professions
ThématiqueOccupational Health and Performance
Établissements canadiensUniversity of Guelph
Organismes subventionnairesnon disponible
Mots-clésPelvisMedicineCadaverCadaveric spasmPelvic fractureIntervention (counseling)Physical therapyRadiologySurgeryNursing

Résumé

récupéré en direct d'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 .

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,194
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0070,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,094
Tête enseignante GPT0,432
Écart entre enseignants0,338 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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