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Record W4409530489 · doi:10.1177/23333936251335531

“This Is My Future?”: Understanding the Lives of Emerging Adult Women Living with Chronic Pain Through a Narrative Inquiry

2025· article· en· W4409530489 on OpenAlexaffabout
Jenise Finlay, Añiela dela Cruz

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2025
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNarrativeDismissalChronic painInvisibilityContext (archaeology)Narrative inquiryQualitative researchPsychologyHealth careAffect (linguistics)MedicineGender studiesSociologyPhysical therapyHistoryPolitical scienceArtLiteratureSocial science

Abstract

fetched live from OpenAlex

Chronic pain disproportionately affects women yet is often underestimated by medical professionals. In Canada, chronic pain rates have risen significantly, particularly among those aged 20 to 29 without other health conditions. However, limited qualitative research focuses on chronic pain exclusively in women under 30. By focusing on gender, this narrative inquiry study examined how societal narratives and stereotypes uniquely affect emerging adult women's experiences of chronic pain, contributing to their dismissal and invisibility in both personal and institutional contexts. Two key narrative threads were co-created with participants through analysis of their stories: silenced, invisible, and locating self with pain, and resisting singular stories of people living with chronic pain. Participants' shared family narratives of dismissal, stories of being silenced in health care, and dominant narratives in the context of age and gender that shaped the participants' stories to live by. This study demonstrates the importance of recognizing people in the midst of living with chronic pain. Understanding unique pain experiences during emerging adulthood can improve treatment options and long-term outcomes for this demographic.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.003
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 head0.074
GPT teacher head0.468
Teacher spread0.393 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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