MétaCan
Menu
Back to cohort
Record W2897007422 · doi:10.1177/1049732318803885

“It’s Hard Work”: A Feminist Political Economy Approach to Reconceptualizing “Work” in the Cancer Context

2018· article· en· W2897007422 on OpenAlexafffund
Cheryl Pritlove, Parissa Safai, Jan Angus, Pat Armstrong, Jennifer M. Jones, Janet Parsons

Bibliographic record

VenueQualitative Health Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoYork UniversitySt. Michael's Hospital
FundersUniversity of Toronto
KeywordsWork (physics)Context (archaeology)PoliticsSociologyGender studiesWomen's workPolitical scienceEngineeringGeography

Abstract

fetched live from OpenAlex

Within mainstream cancer literature, policy documents, and clinical practice, "work" is typically characterized as being synonymous with paid employment, and the problem of work is situated within the "return to work" discourse. The work that patients perform in managing their health, care, and everyday life at times of illness, however, is largely overlooked and unsupported. Drawing on feminist political economy theory, we report on a qualitative study of 12 women living with cancer. Major findings show that the work of patienthood cut across multiple fields of practice and included both paid and unpaid labor. The most prevalent types of work included illness work, body work, identity work, everyday work, paid employment and/or the work of maintaining income, and coordination work. The findings of this study disrupt popular conceptualizations of work and illuminate the nuanced and often invisible work that cancer patients may encounter, and the health consequences and inequities therein.

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)0.061
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.690
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0610.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0010.005

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 head0.794
GPT teacher head0.703
Teacher spread0.090 · 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; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreCommentary

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

Citations28
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueQualitative Health ResearchSame topicObesity and Health PracticesFrench-language works237,207