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Record W3111070793 · doi:10.1177/1074840720967022

Effects of Implementing a Brief Family Nursing Intervention With Hospitalized Oncology Patients and Their Families in Germany: A Quasi-Experimental Study

2020· article· en· W3111070793 on OpenAlexaboutno aff
Manuel Zimansky, Lukasz Stasielowicz, Inga Franke, Hartmut Remmers, Heiko Friedel, Jens Atzpodien

Bibliographic record

VenueJournal of Family Nursing · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsnot available
Fundersnot available
KeywordsNursingIntervention (counseling)MedicineContext (archaeology)Family medicineUnit (ring theory)Nursing carePsychology

Abstract

fetched live from OpenAlex

Family nursing, based on the Calgary Family and Intervention Models, was implemented in a German oncological inpatient unit to promote effective family functioning in the context of cancer care. The objective of this study was to investigate the effects of implementing family nursing care on several psychological and physical outcomes of patients and their family members. A quasi-experimental study with 214 patients with a cancer diagnosis and 122 family members was conducted. Findings indicate that the superiority of family nursing, when compared to traditional care, could not be confirmed with respect to patients' outcomes (psychological burden, social support, satisfaction with care) and family members' outcomes (psychological burden, physical complaints, satisfaction with care). Various factors, such as country-specific structures and challenges in implementing family nursing care on an inpatient unit, may have contributed to these findings. Further replication attempts in similar settings in other countries are needed to shed light on the factors impairing or promoting the implementation of family nursing in practice settings.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.050
GPT teacher head0.399
Teacher spread0.349 · 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 designNon-randomized trial
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

Citations19
Published2020
Admission routes1
Has abstractyes

Explore more

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