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Record W2924607747 · doi:10.1097/ncq.0000000000000400

Including and Training Family Caregivers of Older Adults in Hospital Care

2019· article· en· W2924607747 on OpenAlexaff
Beth Fields, Juleen Rodakowski, Cassandra Leighton, Connie Feiler, Tami Minnier, A. Everette James

Bibliographic record

VenueJournal of Nursing Care Quality · 2019
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsFields Institute for Research in Mathematical Sciences
Fundersnot available
KeywordsDisengagement theoryFamily caregiversThematic analysisNursingPsychologyMedicineQualitative researchGerontology

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the role caregivers play in the delivery of care, the interactions and training methods used with caregivers during an inpatient stay are not clear. PURPOSE: The purpose was to examine interactions and training methods used with caregivers during hospital care. METHODS: A mixed-methods case study was conducted. Observations were summarized and interviews were analyzed using thematic analysis. RESULTS: The frequency of caregiver engagement varied at different points in the care process but was highest among observations during the stay care point. Providers were most commonly using written and verbal instructions to train caregivers. Three themes emerged from the interviews and were described to be both facilitators and barriers to caregiver involvement: experience, time, and relationship. CONCLUSIONS: High-quality person and family-centered care depends upon coordinated efforts among health care systems, providers, patients, and caregivers. Future caregiver initiatives should aim to decrease disengagement, increase assessment, and broaden the use of training methods.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.114
GPT teacher head0.442
Teacher spread0.328 · 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.

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

Citations22
Published2019
Admission routes1
Has abstractyes

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