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Enregistrement W4398165292 · doi:10.1152/physiol.2024.39.s1.2101

Adopting Trauma-Informed Approaches to Teaching & Learning in Health & Exercise Sciences: a Case Study

2024· article· en· W4398165292 sur OpenAlexaff
Meaghan J. MacNutt, Hannah A Connon, Johannah May Black

Notice bibliographique

RevuePhysiology · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueSports injuries and prevention
Établissements canadiensOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésBiomedical sciencesMedicinePsychologyMedical educationPhysiologyNursing

Résumé

récupéré en direct d'OpenAlex

The School of Health & Exercise Sciences at the University of British Columbia Okanagan offers a competency-based undergraduate program that prepares students for professional practice in kinesiology and allied health, clinical exercise physiology, and/or health behaviour change. In delivering this program, we aim to 1) provide a safe and inclusive learning environment for all students, and 2) intentionally prepare students for equity-minded and anti-oppressive professional practice. To support both of these goals, we have recently embarked on a multipronged initiative to implement trauma-informed approaches to teaching and learning across our program. Trauma-informed approaches are critical due to the high prevalence of trauma exposure in both the university student and general adult population. Since people from historically, persistently or systemically marginalized groups are more likely to have experienced trauma, trauma-informed approaches should be considered an essential tool for supporting equity and inclusion in higher education. Finally, trauma-informed practice is especially relevant in a discipline like ours, where learning activities and professional practice commonly involve close examination of the body, touching, and other potentially triggering interactions and events. We have taken several steps to support the adoption of trauma-informed teaching practices in our School. These include characterizing the need for trauma-informed approaches in our laboratory courses, selecting an appropriate framework to guide our recommendations, creating an educator guide for designing and facilitating trauma-informed learning experiences, and developing and launching a training program for instructors and teaching assistants. We have also made progress with integrating learning about trauma-informed approaches into our curriculum. To support backward design, we defined one program-level competency related to trauma-informed practice, with five associated learning outcomes. These learning outcomes have been mapped across the curriculum to support mastery of the competency by graduation. This year, we employed two different instructional approaches (large vs. small group and instructor-led vs. guest expert-led sessions) in one first- and fourth-year course (n=209 and 100 students, respectively). Formative assessments indicate that individual learning outcomes were attained by 72-91% of students. Summative assessments of learning and student evaluations of instruction and perceived learning are forthcoming. Here we describe a comprehensive effort to revise both pedagogy and curriculum in support of trauma-informed teaching and learning in health and exercise sciences. By sharing our process, early successes, and lessons learned, we offer a valuable example for educators and educational designers across disciplines related to human anatomy, physiology, and health. This work is happening on the traditional, ancestral, and unceded territory of the Syilx Okanagan people and is supported by the UBCO School of Health & Exercise Sciences and the UBCO Sexual Violence Prevention & Response Offce. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,862
Score d'incertitude au seuil0,437

Scores Codex et Gemma par catégorie

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

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 Opus0,213
Tête enseignante GPT0,407
Écart entre enseignants0,195 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeAutre devis
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
Publié2024
Routes d'admission1
Résumé présentoui

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