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Record W4281754696 · doi:10.1177/10497323221090805

Perceptions of Inuit Women and Non-Inuit Healthcare Providers on the Implementation of Human Papillomavirus Self-Sampling as an Alternative Cervical Cancer Screening Method in Nunavik, Northern Quebec

2022· article· en· W4281754696 on OpenAlexafffundabout
Christina Santella, Elyse Tratt, Joy Nyamiaka, Louisa Whiteley Tukkiapik, Claire Styffe, Rachel Gamelin, Mary Ellen Macdonald, Paul Brassard

Bibliographic record

VenueQualitative Health Research · 2022
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health Research
KeywordsPapanicolaou stainThematic analysisCervical cancerHealth careMedicinePerceptionFamily medicineHuman papillomavirusQualitative researchNursingPsychologyCancerPolitical scienceSociology

Abstract

fetched live from OpenAlex

Human papillomavirus (HPV) self-sampling offers a cervical cancer (CC) screening alternative that can address certain barriers to the Papanicolaou test. As part of a larger community-based participatory project in Nunavik, Northern Québec, we travelled to two communities to gather perspectives from Inuit women and healthcare professionals (HCPs) on CC screening services and the possible implementation of HPV self-sampling. We held 10 group discussions with 28 Inuit women and 10 semi-structured interviews with 20 HCPs. The thematic analysis extracted themes reflecting one barrier and seven facilitators to accessing CC screening and the implementation of HPV self-sampling in Nunavik. Themes included, though not limited to, language and communication in health settings, access to culturally responsive educational resources on CC, and the noninvasive nature of HPV self-sampling. This study may serve to contribute to the co-development of a strategy for implementation that is designed according to the needs and priorities of the communities.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.257
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.291
GPT teacher head0.612
Teacher spread0.320 · 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

Citations3
Published2022
Admission routes3
Has abstractyes

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