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Record W4415469175 · doi:10.2196/74405

Implementation of Integrative Nursing for Patients With Cancer Receiving Inpatient Care: Protocol for a Convergent Parallel Mixed Methods Evaluation

2025· article· en· W4415469175 on OpenAlexvenueno aff
Lea Raiber, Beate Stock‐Schröer, Johanna Thiele, Klaus Kramer

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsProtocol (science)CancerProgram evaluationEvaluation methodsMEDLINE

Abstract

fetched live from OpenAlex

BACKGROUND: Integrative nursing (IN) involves the application of external naturopathic nursing interventions, such as compresses, embrocations, and therapeutic baths and washes. As part of a university hospital project, patients receiving oncology care in participating wards receive IN interventions as supportive care during their hospital stay as part of a consultation service. OBJECTIVE: This study aims to investigate the acceptance, feasibility, and contextual conditions of implementing IN in inpatient care and to evaluate perceptions, experiences, and perceived impact of IN interventions from multiple stakeholder perspectives. METHODS: We used a convergent parallel mixed methods approach guided by the Consolidated Framework for Implementation Research. The evaluation consists of 5 substudies reflecting multiple perspectives on the project. Patients, relatives, and hospital staff will participate. Substudies include a single-arm pre-post questionnaire (substudy 1) and semistructured interviews (substudy 2) with patients, a cross-sectional survey of relatives (substudy 3), semistructured interviews with health care professionals (substudy 4), and analysis of project-related documentation (substudy 5). Qualitative data will be analyzed using qualitative content analysis, and quantitative data will be analyzed using descriptive and inferential statistical methods. RESULTS: Following separate analyses of each substudy, the findings will be integrated and triangulated to generate overarching meta-inferences. The recruitment phase lasted from October 2023 to January 2025. Data collection was completed in March 2025. As of October 2025, after data verification and plausibility checks, data analysis is ongoing. The first results are expected to be published in 2026. CONCLUSIONS: This study presents a mixed methods research protocol aimed at exploring the implementation of IN within a university hospital setting. It is expected to provide a theory-based contribution to IN implementation in inpatient care while also offering insights into its potential effects at the patient level. The study is anticipated to advance understanding of how IN can be sustainably embedded in hospital practice and to provide actionable insights for improving patient-centered supportive care. TRIAL REGISTRATION: German Clinical Trials Register DRKS00032318; https://drks.de/search/de/trial/DRKS00032318. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/74405.

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)0.135
metaresearch head score (Gemma)0.075
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.135
Threshold uncertainty score0.716

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1350.075
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0040.006
Science and technology studies0.0060.004
Scholarly communication0.0040.003
Open science0.0050.005
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0330.006

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 head0.311
GPT teacher head0.690
Teacher spread0.379 · 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 designQualitative
Domainnot available
GenreProtocol

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

Citations1
Published2025
Admission routes1
Has abstractyes

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