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Record W4246268162 · doi:10.18192/aporia.v3i2.2954

[no title]

2011· article· fr· W4246268162 on OpenAlexvenueaboutno aff
KRISTEN HAASE, ROANNE THOMAS-MACLEAN

Bibliographic record

VenueAporia · 2011
Typearticle
Languagefr
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careMultidisciplinary approachNursingCervical cancerMedicineHealth professionalsPsychologyMedical educationSociologyPolitical scienceCancerSocial science

Abstract

fetched live from OpenAlex

Few studies have assessed healthcare workers' understanding and use of a women's health approach (WHA) in practice, specifically with regards to cervical cancer treatment. Given the dominant biomedical approach, it is important for healthcare workers to be aware of, and feel capable of addressing the gender-specific needs of their patients. The purpose of this study was to assess healthcare workers' understanding of a WHA in a cervical cancer treatment centre in western Canada. Using a feminist case-study method, semi-structured interviews were conducted with nine healthcare professionals of the multidisciplinary team, including: nursing, social work, medicine and radiation therapy. Findings from interviews indicate that healthcare workers did not use a WHA. Analysis brought forward three main barriers to the implementation of a WHA, which stimulated the creation of seven recommendations towards implementation of a comprehensive WHA. The goal of this paper is to disseminate research findings in a way that honours the contribution of the participants from the clinical milieu, and acknowledges the need for creativity, innovation and a 'rethinking' of care delivery for women with cervical cancer.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0270.005

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 head0.090
GPT teacher head0.441
Teacher spread0.350 · 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 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

Citations0
Published2011
Admission routes2
Has abstractyes

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