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Record W7019125076

Exploring indigenous and visible minority women’s access to preventive breast and cervical cancer screening in Canada: a narrative review

2023· dissertation· en· W7019125076 on OpenAlexaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2023
Typedissertation
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPsychological interventionMisinformationGovernment (linguistics)PopulationHealth care
DOInot available

Abstract

fetched live from OpenAlex

Canada’s health care system is founded on the principles of need rather than ability to pay, priding itself for its decentralized, publicly-funded health care system that provides firstdollar coverage for preventive services across all provinces and territories (Allin et al. 2020; Kumachev et al. 2016). Despite best efforts through federal sharing and the indisputable evidence for early preventive mammography and Pap smear screening, profound disparities are found in service availability, utilization and accessibility for Indigenous and Visible Minority women in Canada (Amankwah et al. 2009; Demers et al. 2015; Ferdous et al. 2020). The purpose of this narrative review is threefold: to explore the incidence of breast and cervical cancer amongst Indigenous and Visible Minority populations in Canada, to explore potential barriers associated with the uptake of mammograms and Pap smear tests by these two populations; and to explore achievable interventions to improve access to cancer preventive services for Indigenous and Visible Minority women. This review uses the WHO Social Determinants of Health framework (SDOH) (World Health Organization 2010) rooted in equity to guide the review of the literature, and the Five Dimensions of Accessibility of Healthcare Services (Levesque et al. 2013) to guide the thematic analysis of intervention models. An initial literature search was conducted using Google Scholar and Science Direct databases for articles published from 2000 to 2022, yielding 8,462 articles. After screening for irrelevant titles, and non-Canadian studies, 97 publications remained. After 71 duplicates and publications that did not meet the search criteria were removed, a manual search was performed, yielding a total of 34 publications that were included in the narrative review. Of these 34 articles, 17 articles concerned Visible Minority populations and 17 articles, Indigenous populations. The articles included in this review covered four provinces: (1) British Columbia, (4) Manitoba, (13) Ontario and (2) Quebec, with the remaining 14 referencing Canada as a whole. The studies shared similar trends in breast and cervical cancer incidence and screening uptake for Indigenous and Minority women, with Pap smear uptake being lower than the national threshold and mammography uptake being lower in these populations compared to the rest of the Canadian population. Through thematic analysis, the most common barriers influencing accessibility and underutilization of preventive breast and cervical cancer were found to include socioeconomic status, culture and communication, education, lack of appropriate health care providers, and societal beliefs and attitudes towards cancer screening amongst both populations. Finally, possible interventions were identified in the literature that may inform strategies to achieve more equitable access to healthcare services tailored to Canada’s multicultural society.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.185
Threshold uncertainty score0.372

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0080.014
Science and technology studies0.0030.002
Scholarly communication0.0050.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.061
GPT teacher head0.300
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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