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Record W2766066153 · doi:10.5737/23688076274348355

The psychological impact of the rapid diagnostic centres in cancer screening: A systematic review

2017· review· en· W2766066153 on OpenAlexaffvenue
Mina Singh, Christine Maheu, Teresa J. Brady, Rachel Farah

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2017
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsMcGill UniversityYork University
Fundersnot available
KeywordsMedicineAnxietyBreast cancerCancerSystematic reviewDepression (economics)Clinical psychologyMEDLINEFamily medicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this review is to assess the state of the literature and identify implications for nursing practice and future research on the psychological impact of rapid diagnostic centres (RDC) for women related to breast cancer. A systematic literature review was conducted on the topic and six studies were identified for data extraction and analysis. There is evidence that RDCs decrease short-term anxiety in women undergoing further cancer tests after cancer screening, and who receive a benign diagnosis. There is limited available research on the impact of anxiety on women who receive a diagnosis of cancer in RDCs, but some evidence showed that this sub-group had higher depression in the long term. Nurses need to be aware of the different needs of women undergoing further cancer screening tests after a cancer diagnosis and receiving these results in the same day.

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)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0060.008
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.142
GPT teacher head0.490
Teacher spread0.347 · 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 designSystematic review
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
Published2017
Admission routes2
Has abstractyes

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