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Abstract P6-08-10: Breast cancer in young women in Canada: A needs assessment

2013· article· en· W2029812152 on OpenAlexaboutno aff
Allison Gordon, MJ DeCoteau

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerMedicinePsychosocialFamily medicineCancerPopulationSurvivorship curveNeeds assessmentDiseaseGerontologyGynecologyInternal medicinePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

Abstract The objective of our Needs Assessment research was to determine the role that age plays in the breast cancer experience for patients in Canada. Our Needs Assessment is the first national quantitative data on the experience of breast cancer for young women in Canada, from diagnosis, through treatment and survivorship. The objective for the data is to provide critical evidence based information and benchmarks to stakeholders in the breast cancer field around the challenges facing the young breast cancer patient population, which would help improve patient education, advocacy and support programs for young women. METHODS: An online bilingual (English and French) quantitative survey consisting of 88 questions. The survey was open to women who had a breast cancer diagnosis (first time or recurrence) in the previous 6 years. 574 women responded to the survey. 372 (65%) were ages 20-45, 202 (35%) were ages 46-69. The results were analyzed to look at significant differences in the answers from younger and older respondents. The Needs Assessment Report was published in March 2013. RESULTS: While the impact of breast cancer is stressful for women of all ages, the data from our Needs Assessment shows that younger women are particularly vulnerable to the negative psychosocial affects of the disease. Younger women (20-45) were more likely to feel their doctor did not take them seriously when they presented a possible symptom of breast cancer, such as a breast lump (17% vs. 10%) and were more likely to be dissatisfied with the diagnostic process, with 26% of women 20-29 expressing dissatisfaction (versus 19% overall). Younger women had a more difficult time navigating the health care system (36% reported difficulty vs. 29%). Of women 20-39 who expressed concerns about the impact of treatment on fertility, less than half (49%) were referred to a fertility specialist. Younger women rated very highly the importance of connecting with another women with breast cancer their own age (88% vs. 70%) but only 64% of younger women made this connection. Younger women indicated stronger fears of cancer recurrence than their older counterparts, and stronger concerns about the long term effects of treatment. In addition to needing support for their partners and children, younger women were more likely to require support for their parents (42% vs. 22%) although less than 1/3 of their parents received support. Younger women had a more difficult time transitioning from regular to occasional medical monitoring (59% vs. 42%) and were more likely to report a reduced interest in sexual activity due to treatment (67% vs. 54%). CONCLUSION: Our needs assessment data shows that age does have an impact on the breast cancer experience and creates challenges that are not currently being met by healthcare systems. We attribute two key factors to younger women having a more difficult breast cancer experience than older women: aggressiveness of cancer treatment and life-stage. The data shows that despite a growing array of peer support based interventions and community resources available to young women, many of the concerns of young breast cancer patients are still not being met. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P6-08-10.

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.499

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.005
Science and technology studies0.0070.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.365
Teacher spread0.339 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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