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Record W2811506400 · doi:10.1177/2333393618783632

“They Should Be Asking Us”: A Qualitative Decisional Needs Assessment for Women Considering Cervical Cancer Screening

2018· article· en· W2811506400 on OpenAlexaffabout
Brianne Wood, Virginia L. Russell, Ziad El‐Khatib, Susan McFaul, Monica Taljaard, Julian Little, Ian D. Graham

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2018
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsOttawa HospitalUniversité du Québec en Abitibi-TémiscamingueUniversity of ManitobaUniversity of Ottawa
Fundersnot available
KeywordsCervical screeningMedicineCervical cancer screeningFamily medicineDecision aidsCervical cancerPap testPreferenceHealth careModalitiesHuman papillomavirusQualitative researchCancer screeningNursingGynecologyCancerAlternative medicine

Abstract

fetched live from OpenAlex

In this study, we examine from multiple perspectives, women's shared decision-making needs when considering cervical screening options: Pap testing, in-clinic human papillomavirus (HPV) testing, self-collected HPV testing, or no screening. The Ottawa Decision Support Framework guided the development of the interview schedule. We conducted semi-structured interviews with seven screen-eligible women and five health care professionals (three health care providers and two health system managers). Women did not perceive that cervical screening involves a "decision," which limited their knowledge of options, risks, and benefits. Women and health professionals emphasized how a trusted primary care provider can support women making a choice among cervical screening modalities. Having all cervical screening options recommended and funded was perceived as an important step to facilitate shared decision making. Supporting women in making preference-based decisions in cervical cancer screening may increase screening among those who do not undergo screening regularly and decrease uptake in women who are over-screened.

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)0.013
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.429
GPT teacher head0.642
Teacher spread0.214 · 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

Citations18
Published2018
Admission routes2
Has abstractyes

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