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Record W2513459254 · doi:10.1177/0898010116665447

Holistic Nurses’ Use of Energy-Based Caring Modalities

2016· article· en· W2513459254 on OpenAlexaff
Noreen Frisch, Howard K. Butcher, Diana Campbell, Dickon Weir‐Hughes

Bibliographic record

VenueJournal of Holistic Nursing · 2016
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsModalitiesHolistic healthNursingModality (human–computer interaction)Treatment modalityMedicineEnergy (signal processing)Life spanTheme (computing)Nursing practicePsychologyMedical educationAlternative medicineGerontologyComputer scienceSociology

Abstract

fetched live from OpenAlex

As part of a study of a larger study of self-identified holistic nurses, researchers asked nurses to describe practice situations where energy-based modalities (EBMs) were used. Four hundred and twenty-four nurses responded by writing free-text responses on an online survey tool. The participants were highly educated and very experienced with 42% holding graduate degrees and 77% having over 21 years of practice. Conventional content analysis revealed four themes: EMBs are 1) caring modalities used to treat a wide range of identified nursing concerns; 2) implemented across the life span and to facilitate life transitions; 3) support care for the treatment of specific medical conditions; and 4) Use of EBMs transcend labels of 'conditions' and are used within a holistic framework. The fourth theme reveals a shared vision of nursing work such that the modality becomes secondary and the need to address the 'whole' at an energetic level emerges as the primary focus of holistic nursing.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.164
GPT teacher head0.422
Teacher spread0.258 · 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 designObservational
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
Published2016
Admission routes1
Has abstractyes

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