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Record W2049608102 · doi:10.1080/07399332.2014.959168

Strategies for Increasing Cervical Cancer Screening Amongst First Nations Communities in Northwest Ontario, Canada

2014· article· en· W2049608102 on OpenAlexafffundabout
Marion Maar, Pamela Wakewich, Brianne Wood, Alberto Severini, Julian Little, Ann N. Burchell, Gina Ogilvie, Ingeborg Zehbe

Bibliographic record

VenueHealth Care For Women International · 2014
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsOntario HIV Treatment NetworkPublic Health Agency of CanadaThunder Bay Regional Research InstituteUniversity of OttawaLakehead UniversityBC Centre for Disease ControlPublic Health OntarioNOSM University
FundersCanadian Institutes of Health Research
KeywordsCervical cancerMedicineMEDLINEGeographyCancerEnvironmental healthSocioeconomicsDemographyGerontologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

The high burden of cervical cancer in Indigenous populations worldwide is due to underscreening and inadequate follow-up. Using qualitative, participatory action research, we interviewed health care staff to identify ways to increase screening recruitment in First Nations communities in Northwest Ontario, Canada. Our findings suggest the value of a multilevel social-ecological model to promote behavioral changes at the community, health care service and stakeholder, and decision-maker level. Participants emphasized the central role of First Nations women as nurturers of life and for the well-being of their family members. They stressed the importance of building awareness and motivation for cervical cancer screening through various activities including continuous education, hosting screening events specifically for women, improving the attitude and service of health care providers, and promoting screening tools and policies that complement and are respectful of First Nations women.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.032
GPT teacher head0.350
Teacher spread0.318 · 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 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

Citations29
Published2014
Admission routes3
Has abstractyes

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