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Record W3048601600 · doi:10.3917/rsi.141.0038

Mieux saisir les difficultés d’adaptation des personnes âgées insuffisantes cardiaques en transition de l’hôpital vers le domicile à partir des expériences vécues et d’un éclairage théorique

2020· article· fr· W3048601600 on OpenAlexaff
M. Hardy, Clémence Dallaire

Bibliographic record

VenueRecherche en soins infirmiers · 2020
Typearticle
Languagefr
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPsychological interventionTriangulationPsychologyGrounded theoryQualitative researchQuality of life (healthcare)Nursing Interventions ClassificationNursingAdaptation (eye)GerontologyMedicineSociology

Abstract

fetched live from OpenAlex

Heart failure is one of the most common reasons for hospitalization in older people, and the hospital-to-home transition can be unsuccessful for these patients. Existing care programs focus primarily on the physiological aspects of the disease and are rarely based on theory. Using Roy's adaptation model (1), the aim of this study was to develop a thorough understanding of the adaptation difficulties and factors that influence how well elderly patients with chronic heart failure cope with the hospital-to-home transition, in order to develop a nursing interventions program. Based on the process proposed by Sidani and Braden (2011), this qualitative descriptive study adopted a deductive approach, with the use of intermediary theories and empirical data, as well as an inductive approach, where older people with chronic heart failure (n=7), caregivers (n=6), and healthcare professionals (n=14) participated in semi-structured individual interviews. The triangulation of data highlights the difficulties and factors influencing adaptation at the physical, psychological, and social levels. Gaining a better understanding of the experience of older people with heart failure when it comes to their transition from hospital to home, and doing so with a holistic vision, provides information for interventions that can contribute to better management of chronic disease and a better quality of life for these elderly 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)0.009
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0020.004
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 head0.356
GPT teacher head0.477
Teacher spread0.121 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2020
Admission routes1
Has abstractyes

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