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Musculoskeletal Manifestations of Physical Abuse After Intimate Partner Violence

2006· article· en· W2014824869 on OpenAlexaff
Mohit Bhandari, Sonia Dosanjh, Paul Tornetta, David Matthews

Bibliographic record

VenueThe Journal of Trauma: Injury, Infection, and Critical Care · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsMcMaster University Medical CentreMcMaster University
Fundersnot available
KeywordsDomestic violencePhysical abuseSexual abuseMedicinePsychological abusePoison controlPsychiatrySuicide preventionInjury preventionClinical psychologyMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: Domestic violence is the most common cause of nonfatal injury to women in the United States, with an estimated cost of $50 billion annually. Little is known about the spectrum of musculoskeletal injuries in victims of domestic violence. We examined the characteristics of abused women, the prevalence of musculoskeletal injuries, and the variables associated with increasing frequency of physical violence against women. METHODS: We identified all female survivors of intimate partner violence who were referred to the Minnesota Domestic Abuse Program from January 1, 2002, through December 31, 2003. Characteristics of each woman's background, abuse history, and injuries were obtained by a trained program therapist in an in-depth, 2-hour intake interview. Specific data forms were completed for each interview. Five forms of experienced abuse were explored (physical, emotional, psychological, sexual, and financial). Injuries were subcategorized as (1) head and neck, (2) musculoskeletal, (3) chest, (4) abdomen, and (5) skin (integumentary system). We conducted regression analyses to determine factors associated with the frequency of physical abuse. RESULTS: Of 270 potentially eligible women, 263 (97%) with complete records were included. Women were commonly Caucasian (62%) in their third decade of life with one or more children (87%). A history of abuse was recalled by over half of the women (54%). The most prevalent forms of abuse were emotional (84%), psychological (68%), physical (43%), sexual (41%), and financial (38%). Child protective services were concomitantly involved in half of the women living in abusive relationships. Among those women who reported physical abuse, 36% sought medical attention. We identified 144 injuries in 218 physically abused women. Head and neck injuries were the most prevalent after intimate partner violence (40%). Musculoskeletal injuries were the second most common manifestation of intimate partner violence (28%). The spectrum of injuries included sprains (n = 21 injuries), fracture/dislocations (n = 17 injuries), and foot injuries (n = 2 injuries). Our analysis identified seven variables associated with increasing physical abuse frequency. These included (1) younger age (p = 0.04); (2) shorter length of relationship (p = 0.049); (3) emotional abuse (p = 0.02); (4) psychological abuse (p = 0.003); (5) sexual abuse (p = 0.004); (6) drug dependency (p = 0.05); and (7) alcohol dependency (p = 0.045). CONCLUSIONS: Among women presenting to a domestic violence therapy program seeking counseling, head and neck and musculoskeletal injuries were most common. Frequency of physical abuse was most likely to be associated with younger women who are in short-term relationships, have chemical and alcohol dependency, and concomitant emotional, psychological, and sexual abuse. Recognizing musculoskeletal injuries in women as a potential result of intimate partner violence is warranted. Identifying children exposed to abusive situations may further alert treating surgeons to the potential for intimate partner violence in the mother.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.339
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations134
Published2006
Admission routes1
Has abstractyes

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