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Record W1976335560 · doi:10.12968/bjon.2014.23.1.40

Complementary and alternative medicine in oncology nursing

2014· article· en· W1976335560 on OpenAlexaff
Salima Somani, Fauziya Ali, Tazeen Saeed Ali, Nasreen Lalani

Bibliographic record

VenueBritish Journal of Nursing · 2014
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineInternal medicineOncologyOncology nursingFamily medicineAlternative medicineNursingPrayerHealth careIntegrative medicineNurse educationPathology

Abstract

fetched live from OpenAlex

Use of complementary and alternative medicine (CAM) has increased globally, particularly among oncology patients. This study investigated the knowledge, experience and attitudes of oncology nurses towards CAM. A quantitative study was conducted in tertiary care hospitals in Karachi, Pakistan, where 132 oncology nurses were surveyed. The survey revealed that more than 50% of nurses had never heard about many of the CAM therapies used in Pakistan. Approximately 65% of the nurses had knowledge about prayer and less than 30% had experience of CAM education or training. In addition, the majority of nurses had seen patients using CAM and felt that their health status could be enhanced with the use of CAM. This study showed that oncology nurses had a positive experience of and attitude towards CAM, although they needed to enhance their knowledge of it to maximise patient satisfaction and quality of care.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.070
GPT teacher head0.411
Teacher spread0.340 · 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 designNot applicable
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

Citations15
Published2014
Admission routes1
Has abstractyes

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