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Record W3126776757 · doi:10.4037/ccn2021188

Nursing Interventions to Reduce Stress in Families of Critical Care Patients: An Integrative Review

2021· article· en· W3126776757 on OpenAlexaff
Valérie Lebel, Sylvie Charette

Bibliographic record

VenueCritical Care Nurse · 2021
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsMedicinePsychological interventionNursing Interventions ClassificationNursingMEDLINE

Abstract

fetched live from OpenAlex

BACKGROUND: Having a family member admitted to an intensive care unit is a stressful experience that may lead to psychological symptoms including depression, anxiety, and posttraumatic stress disorder. OBJECTIVE: To better understand the phenomenon of stress experienced by families of intensive care unit patients and identify nursing interventions that may help reduce it. METHODS: An integrative literature review was performed to identify principal stressors for families of patients receiving care in neonatal, pediatric, and adult intensive care units and recommended nursing interventions. RESULTS: The principal stressors in the 3 types of intensive care units were change in parental role or family dynamics, appearance and behavior of the patient, the care setting, and communication with the health care staff. Nursing interventions should focus on valuing the role of family members in patient care, improving communication, and providing accurate information. CLINICAL RELEVANCE: Family members of intensive care patients will benefit from nursing interventions that adequately acknowledge and address the stress they experience. CONCLUSION: Nurses play a crucial role in helping to reduce the stress experienced by family members of intensive care unit patients.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.041
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.121
GPT teacher head0.516
Teacher spread0.396 · 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 teacher head, not a consensus.

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

Citations22
Published2021
Admission routes1
Has abstractyes

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