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Record W3210164731 · doi:10.1016/j.pmedr.2021.101622

Care in the Community: Opportunities to improve cancer screening uptake for people living with low income

2021· article· en· W3210164731 on OpenAlexaffabout
Aïsha Lofters, Natalie A. Baker, Ann Marie Corrado, Andrée Schuler, Allison Rau, Nancy N. Baxter, Fok‐Han Leung, Karen Weyman, Tara Kiran

Bibliographic record

VenuePreventive Medicine Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsInstitute for Work & HealthWomen's College HospitalCancer Care OntarioPublic Health OntarioUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsOutreachCancer screeningContext (archaeology)Focus groupMedicinePrimary careExploratory researchFamily medicineCancerGerontologyNursingBusinessPolitical scienceSociologyGeographyMarketing

Abstract

fetched live from OpenAlex

Despite organized provincial cancer screening programs, people living with low income consistently have lower rates of screening in Ontario, Canada than their more socioeconomically advantaged peers. We previously published results of a two-phase, exploratory qualitative study involving both interviews and focus groups whose objective was to integrate knowledge of people living with low income on how to improve primary care strategies aimed at increasing cancer screening uptake. In the current paper, we report previously unpublished findings from that study that identify how taking a community outreach approach in primary care may lead to increased cancer screening uptake among people living with low income. Participants told us that they saw value in a community outreach approach to cancer screening. They recommended specific actionable approaches, in particular, mobile community-based screening and community information sessions, and recommended taking an ethno-specific lens depending on the communities being targeted. Participants expressed a desire for primary care providers to go out into the community to learn more about the whole patient, such as could be achieved with home visits, but they simultaneously believed that this may be challenging in urban settings and in the context of perceived physician shortages. Models of primary care that provide support to an entire local community and provide some of their services directly in that community may have a meaningful impact on cancer screening for socially marginalized groups.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.086
GPT teacher head0.363
Teacher spread0.277 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2021
Admission routes2
Has abstractyes

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