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Record W4286679907 · doi:10.3138/ptc-2021-0084

Impact of Income on Physical Concerns, Help Seeking, and Unmet Needs of Adult Cancer Survivors

2022· article· en· W4286679907 on OpenAlexaffvenue
Irene Nicoli, Gina Lockwood, Lauren Fitch, Christopher J. Longo, Margaret I. Fitch

Bibliographic record

VenuePhysiotherapy Canada · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsUniversity of TorontoMcMaster UniversityCARE Canada
Fundersnot available
KeywordsMedicineCancerLow incomeProstate cancerGerontologyBreast cancerCancer treatmentHousehold incomeColorectal cancer

Abstract

fetched live from OpenAlex

Purpose: Cancer treatment can have consequences for individuals which may have profound impact on daily living. Accessing assistance can be problematic. This study explores associations between income and concerns, help-seeking, and unmet needs related to physical changes following cancer treatment. Method: A national survey was conducted with cancer survivors about experiences with follow-up care one to three years after treatment. We report a trend analysis describing associations between income and cancer survivors' concerns, help-seeking, and unmet needs related to physical changes after treatment. Results: In total 5,283 cancer survivors between 18 and 64 years responded, of which 4,264 (80.7%) indicated annual household income. The majority of respondents were survivors of breast (34.4%), colorectal (15.0%), and prostate (14.0%) cancers. Over 90% wrote about experiencing physical changes following cancer treatment. Survivors with low annual household incomes of less than $25,000 (CAN) reported the highest levels of concern about multiple physical changes and were more likely to seek help to address them. Conclusions: Cancer survivors can experience various physical challenges and unmet needs following cancer treatment and difficulty obtaining relevant help across all income levels. Those with low income are more severely affected. Financial assessment and tailored follow-up are recommended.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.013
GPT teacher head0.263
Teacher spread0.250 · 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
Published2022
Admission routes2
Has abstractyes

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