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Record W4386120676 · doi:10.5430/jnep.v13n12p27

Traditional support groups for women with breast cancer: A review of the literature

2023· review· en· W4386120676 on OpenAlexvenueno aff
Wafaa Shehada, Kathleen Benjamin, Sadia Munir, Nima Ali

Bibliographic record

VenueJournal of Nursing Education and Practice · 2023
Typereview
Languageen
FieldSocial Sciences
TopicMental Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsCINAHLPsycINFOBreast cancerPsychological interventionMedicineInclusion and exclusion criteriaSocial supportMEDLINEContext (archaeology)AnxietyInclusion (mineral)Family medicineClinical psychologyCancerAlternative medicineNursingPsychologyPsychiatryPsychotherapistInternal medicineSocial psychologyPathology

Abstract

fetched live from OpenAlex

Background and objective: Breast cancer is the most common cancer among women worldwide and it is by far the most common cancer of women in Qatar. Nurses can play an important role in developing and implementing support groups for women with breast cancer. The main objective of this literature review was to identify the context of information to develop a support group to meet the needs of women with breast cancer in Qatar.Methods: The following databases were searched: Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, MEDLINE, EMBASE, and PsycINFO. After applying the inclusion and exclusion criteria, 25 studies were retained for this review. Results: The synthesis and integration of the literature revealed that traditional support groups can impact women with breast cancer across the physical, psychological, spiritual, and social domains. Various outcomes such as fatigue and anxiety were measured and several different types of interventions were used such as education, relaxation, and goal setting. Overall, the interventions had a positive impact on some of the outcomes.Conclusions: This review highlights the need to develop and implement a support group program for women with breast cancer in Qatar.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.920
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.154
GPT teacher head0.511
Teacher spread0.357 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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