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Record W2771129743 · doi:10.1177/1054773817748400

Family Perceptions of Family Nursing in a Magnet Institution During Acute Hospitalizations of Older Adult Patients

2017· article· en· W2771129743 on OpenAlexaboutno aff
Kara Misto

Bibliographic record

VenueClinical Nursing Research · 2017
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsnot available
FundersRhode Island College
KeywordsIntervention (counseling)MedicineFamily memberNursingPerceptionFamily caregiversSocial supportFamily medicineFamily supportPsychologyPhysical therapy

Abstract

fetched live from OpenAlex

Family involvement during hospitalizations of older adults with chronic illnesses may benefit both patients and family. However, there is a limited amount of research focused on families of older adults managing chronic illness. This study describes family member perceptions of the relationship between family and nurses when an older adult with diabetes is hospitalized in a Magnet institution. The Calgary Family Intervention Model guided the study. A family member of 60 older adult patients completed the Family Function, Family Health, and Social Support Instrument. The results revealed positive perceptions from family members regarding their perceptions of family nursing practice. Family health, however, was found to decrease slightly as loved ones are hospitalized more frequently, and is an area where nurses may provide improved social support. Future research might target the implementation of a targeted family-level intervention designed to improve family outcomes as well as family nursing practice.

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)0.007
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.200
GPT teacher head0.563
Teacher spread0.362 · 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
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

Citations2
Published2017
Admission routes1
Has abstractyes

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