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Record W3087728676 · doi:10.1136/bmjspcare-2020-002313

Exploring the reasons cancer survivors do not seek help for their concerns: a descriptive content analysis

2020· article· en· W3087728676 on OpenAlexafffundabout
Margaret I. Fitch, Irene Nicoll, Gina Lockwood

Bibliographic record

VenueBMJ Supportive & Palliative Care · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Toronto
FundersPartenariat Canadien Contre Le Cancer
KeywordsSurvivorship curveDescriptive statisticsPsychologyPopulationContent analysisQuality of life (healthcare)MedicineHelp-seekingHealth careNursingFamily medicineMedical educationPsychiatryMental healthPolitical scienceSociologyEnvironmental health

Abstract

fetched live from OpenAlex

PURPOSE: To understand reasons why adult cancer survivors do not seek assistance as they transition from the end of cancer treatment to follow-up care. Understanding these reasons should inform survivorship care, help reduce the burden of suffering and increase quality of life for survivors. METHODS: A national survey was conducted in collaboration with ten Canadian provinces to identify unmet needs and experiences with follow-up for cancer survivors between one and 3 years post-treatment. The survey included open-ended questions to allow respondents to add topics of importance and details that offered a deeper insight into their experiences. This publication presents the analysis of the quantitative data and open-ended responses regarding reasons why the adult cancer population does not seek help with their concerns. RESULTS: In total, 13 319 respondents answered the question about seeking help. 87% had a physical concern of which 76% did not seek help; 77% had an emotional concern of which 82% did not seek help; and 45% had a practical concern of which 71% did not seek help. Frequently identified reasons for not seeking help included being told it was normal and not thinking anything could be done, not wanting to ask, not thinking services were available, handling it on their own and not thinking it was serious enough to seek help. CONCLUSIONS: Survivors have multiple reasons for not seeking help for their concerns. These findings can be useful to healthcare providers in proactively identifying and addressing the needs of these survivors.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.348
GPT teacher head0.379
Teacher spread0.031 · 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.

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

Citations8
Published2020
Admission routes3
Has abstractyes

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