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Record W2171763306 · doi:10.1017/s1460396908006377

Assessing and supporting body image and sexual concerns for young women with breast cancer: a literature review

2008· review· en· W2171763306 on OpenAlexaffabout
Cher Kinamore

Bibliographic record

VenueJournal of Radiotherapy in Practice · 2008
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsPsychological interventionHuman sexualityBreast cancerMedicineIntervention (counseling)CancerFamily medicineClinical psychologyNursingInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background: Breast cancer is the most common cancer and most common cancer cause of death in women aged 20–49 years in Canada. Developing a functional definition of ‘young’ is imperative in assessing and providing appropriate emotional support to the unique body image and sexuality concerns facing ‘young’ women with breast cancer. These concerns require proper assessment in order to provide appropriate interventions. Aims and objectives: To seek a functional definition of ‘young’ and to determine what body image and sexuality assessment tools and interventions are the most appropriate for young women with breast cancer. Methods: A literature search was undertaken to determine what body image and sexuality assessment tools and interventions are available and relevant to young women with breast cancer. Also, the assessment and interventions available to this patient cohort in the author's clinic were explored. Conclusions: Body image and sexuality questionnaires encourage young women and health-care providers (HCPs) to openly discuss these issues. Annon's PLISSIT model is an assessment and intervention strategy that enables HCPs to adequately assess and refer young women to suitable programs such as support groups and counsellors. The multi-disciplinary team should provide continuous emotional assessment and support throughout the cancer journey by collaborating to develop the best interventional strategies to the patient and her family.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.415
Teacher spread0.388 · 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 designOther design
Domainnot available
GenreReview

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

Citations9
Published2008
Admission routes2
Has abstractyes

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