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Record W3003044252 · doi:10.1177/1049732319900163

Women’s Gendered Experiences of Traumatic Brain Injury

2020· article· en· W3003044252 on OpenAlexafffundabout
Alexis Fabricius, Andrea D’Souza, Vanessa Amodio, Angela Colantonio, Tatyana Mollayeva

Bibliographic record

VenueQualitative Health Research · 2020
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchToronto Rehabilitation Institute
KeywordsTraumatic brain injuryPsychologyClinical psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Despite recognizing that women have worse outcomes after traumatic brain injury (TBI), little is known about how gender influences their experiences of this critical injury. Past research has been dominated by androcentrism and quantitative approaches, leaving the lived experience of women with TBI insufficiently examined. To gain insight into their experiences, this qualitative study interviewed 19 Canadian women with mild and moderate-to-severe TBIs. Applying a thematic analysis, we discerned three themes: Gender prevails considers choosing to do gender over complying with physician advice; Consequences of TBI impeding performativity explores how women frame themselves as terrible people for being unable to do gender post-TBI; and Perceptions of receiving care looks at gendered caregiving expectations. These results broadly align with research on how doing gender influences recovery and health outcomes. We discuss the implications of our findings for knowledge translation, future research on women’s TBI recovery, and clinical practice.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.578

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0150.014
Scholarly communication0.0060.003
Open science0.0010.006
Research integrity0.0010.002
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 head0.732
GPT teacher head0.626
Teacher spread0.106 · 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 designQualitative
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

Citations29
Published2020
Admission routes3
Has abstractyes

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