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Record W4410917566 · doi:10.70205/jptmh.v2i1.9725

The Importance of Trauma-Informed Care: A Call to Action for Physical Therapist Practice, Education, Research, and Advocacy

2025· article· en· W4410917566 on OpenAlexaff
Rose M. Pignataro, Joe Tatta, Megan Hamilton, Ginny Moorer, Rachel Stiltner

Bibliographic record

VenueJournal of Physiotherapy in Mental Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsWorkers Compensation Board of British Columbia
Fundersnot available
KeywordsCall to actionPhysical therapistAction (physics)MedicinePsychologyNursingPsychotherapistMedical educationPhysical therapyBusiness

Abstract

fetched live from OpenAlex

Trauma exposure is associated with a host of biopsychosocial effects, including premature mortality, compromised physical/mental health, substance misuse, and addiction; 90% of American adults report at least one significant lifetime traumatic incident. Disparate trauma exposure among people with physical disabilities, lower socioeconomic status, rural residence, and/or racial/ethnic minority status creates a strong rationale supporting trauma-informed care (TIC) in promoting health equity. Although physical therapist (PT) practice, education, and research do not routinely integrate TIC, public health needs and the opportunity to advance health equity compel greater professional involvement in addressing trauma and its impact on overall wellness. This perspective describes (1) the biopsychosocial impact of trauma; (2) screening and assessment of trauma exposure and its impact; (3) TIC in physical therapy practice; (4) the impact of unaddressed trauma; and 5) TIC in physical therapy education, research, and advocacy. Physical therapy professionals should apply this information to address the gap in trauma-informed care for individuals, families, groups, and communities.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.062
GPT teacher head0.600
Teacher spread0.538 · 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 designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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