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

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

Bibliographic record

VenuePhysiology · 2024
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsBiomedical sciencesMedicinePsychologyMedical educationPhysiologyNursing

Abstract

fetched live from 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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.213
GPT teacher head0.407
Teacher spread0.195 · 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 designOther design
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
Published2024
Admission routes1
Has abstractyes

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