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Record W2804561264 · doi:10.5430/jnep.v8n10p86

Comfort of the hospitalized elderly

2018· article· en· W2804561264 on OpenAlexvenueno aff
Teresa Cristina Pantozzi Silveira, Raquel da Silva Pereira, Carla Alexandra Amorim COLAÇO, Rita Marques, Patrícia Pontífice-Sousa

Bibliographic record

VenueJournal of Nursing Education and Practice · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Exploratory researchQualitative researchPsychologyPerceptionMedicinePopulationNursingGerontology

Abstract

fetched live from OpenAlex

The purpose of this pilot study is to identify the specifics of comfort in the hospitalized elderly population. This is a descriptive-exploratory pilot study, with a qualitative approach. Data was collected between January and February 2018, being included 12 elderly participants hospitalized in the pulmonology/oncology department. Semi-structured audio-recorded interviews were conducted to obtain the data. The central theme of the comfort phenomenon for the hospitalized elderly individuals comprises four categories that represent the perceptions of the subjects, namely: needs that were felt/experience’s context; intervenients’ role/experience’s context; ways and means of causing comfort/discomfort; attributes associated with the concept of comfort/discomfort. The analysis of each of these categories showed the importance of developing skills, in order to satisfy the comfort needs of the hospitalized elderly. The elderly constitute a group which is socially more vulnerable and fragile. For this reason, nurses and students should be available to provide relief, well-being and comfort to this population with specific needs. The findings of this study reinforce the results of previous research efforts, highlighting categories and subcategories that allow to achieve a balance between needs, expectations and wishes, and an integrated comforting care that should be considered and object of deep research by nursing students.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.127
GPT teacher head0.542
Teacher spread0.415 · 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 designNot applicable
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

Citations4
Published2018
Admission routes1
Has abstractyes

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