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Abstract 85: The Social and Economic Impact of a Cervical Cancer Diagnosis on Women and Children in Uganda

2023· article· en· W4378966290 on OpenAlexaff
Hallie Dau, Shamim Nakyazze, Priscilla Naguti, Avery McNair, Beth A. Payne, Marianne Vidler, Joel Singer, Laurie Smith, Jackson Orem, Carolyn Nakisige, Gina Ogilvie

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2023
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsBC Centre for Disease ControlUniversity of British Columbia
Fundersnot available
KeywordsCervical cancerMedicineCancerDescriptive statisticsFamily medicineGynecologyDemographyInternal medicine

Abstract

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Abstract Purpose: Cervical cancer is the second most common cancer for women living in low- and middle-income countries (LMIC). In Uganda 40% of all female cancer cases are cervical cancer. There is a need for more research on the social and economic impacts of cervical cancer in LMICs in order to provide evidence for expanded cervical cancer screening programs. The goal of this study is the understand the economic impact of cervical cancer on women and children in Uganda. Methods: Data collection for this study began in September and will continue until December 2022. Participants were recruited by nurses at two clinics at the Uganda Cancer Institute in Kampala and Jinja. Participants were eligible if they are being treated for cervical cancer and speak either English, Lusoga, Luo, Runyankore, or Luganda. Each participant completed a 45-minute orally administered survey on REDCap. Descriptive statistics using counts and frequencies were used to describe primary outcomes which include changes in a child’s education and a family’s economic status. Ethical approval was obtained from the University of British Columbia, Uganda Cancer Institute, and Uganda National Council for Science & Technology. Results: To date 67 participants have completed the survey and 57 indicated that they had children living in their household. The mean age of participants is 49 years. The majority of participants are married, have a primary school education or less, and were diagnosed with stage II or stage III cancer. Women reported traveling up to 14 hours to receive cancer treatment and 28% (n=19) brought their child with them to the clinic because they could not find childcare. In all, 93% (n=62) of women indicated that they have paid out of pocket for some type of medical care with 15% (n=9) of women noting that they stopped at least one treatment because of the cost. Approximately 15% of women reported that they cut down on food consumed and withdrew their child from school to help pay for cancer care. Conclusion: We found that a cervical cancer diagnosis has not only on women, but their children as well, confirming that while largely preventable, cervical cancer has far-reaching impact beyond the woman diagnosed. The results of this study can be used to provide further evidence of the urgent need expand cervical cancer screening programs not only in Uganda, but similar countries as well. This in turn will help contribute to the eventual global elimination of cervical cancer. Citation Format: Hallie Dau, Shamim Nakyazze, Priscilla Naguti, Avery McNair, Beth Payne, Marianne Vidler, Joel Singer, Laurie Smith, Jackson Orem, Carolyn Nakisige, Gina Ogilvie. The Social and Economic Impact of a Cervical Cancer Diagnosis on Women and Children in Uganda [abstract]. In: Proceedings of the 11th Annual Symposium on Global Cancer Research; Closing the Research-to-Implementation Gap; 2023 Apr 4-6. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(6_Suppl):Abstract nr 85.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.078
GPT teacher head0.452
Teacher spread0.374 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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