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Record W2063184393 · doi:10.5539/gjhs.v4n5p20

The Intensity of Intensive Care: A Patient’s Narrative

2012· article· en· W2063184393 on OpenAlexvenueno aff
Alida Herbst, Cornelia Maria Drenth

Bibliographic record

VenueGlobal Journal of Health Science · 2012
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeIntensity (physics)Intensive careNarrative reviewPsychologyMedicineHistoryIntensive care medicineArtPsychotherapistLiteraturePhysicsOptics

Abstract

fetched live from OpenAlex

This qualitative study involved action research to explore one woman's narrative of awareness, emotions and thoughts during treatment in an intensive care unit (ICU). The overarching aim is to increase insight into the thoughts, feelings and bio-psychosocial needs of the patient receiving treatment in ICU. Data was collected by means of narrative discourse analysis. Literature on the psychosocial and spiritual implications of ICU treatment is limited, and often patients have no recall of their treatment in an ICU at all. Documenting the illness narrative of this individual case is valuable as the participant could recall a certain amount of awareness, thoughts and emotions. These experiences included delirium, anxiety, helplessness, frustration and uncertainty. Once sedation was decreased, the patient's consciousness increased and she was confronted with thoughts and emotions that were unrealistic and frightening. It was found in this study that the opportunity to share a narrative on the emotions and awareness during treatment in an ICU had cathartic value and the participant suffered little symptoms of post traumatic stress syndrome, often associated with long term treatment in an ICU. Further research on this topic is necessary to improve ICU treatment, not only on a physical level, but with emphasis on the psychosocial and spiritual needs of the patient.

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)0.019
metaresearch head score (Gemma)0.046
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
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 head0.106
GPT teacher head0.524
Teacher spread0.418 · 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

Citations28
Published2012
Admission routes1
Has abstractyes

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