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Record W3200600369 · doi:10.1097/njh.0000000000000803

Effects of Bathing in a Tub on Physical and Psychological Symptoms of End-of-Life Cancer Patients

2021· article· en· W3200600369 on OpenAlexaboutno aff
Eriko Hayashi, Maho Aoyama, Fumiyasu Fukano, Junko Takano, Yoichi Shimizu, Mitsunori Miyashita

Bibliographic record

VenueJournal of Hospice and Palliative Nursing · 2021
Typearticle
Languageen
FieldHealth Professions
TopicTherapeutic Uses of Natural Elements
Canadian institutionsnot available
Fundersnot available
KeywordsBathingMedicineAnxietyDepression (economics)Palliative careCancerPhysical therapyInternal medicinePsychiatryNursing

Abstract

fetched live from OpenAlex

This observational, controlled study explored the effects of bathing on the physical and psychological aspects of terminal cancer patients on a palliative care ward. With nurses' assistance, the patients evaluated and recorded the severity of their symptoms at 10:00 am, 30 minutes after initial bathing, and at 5:00 pm. The bathing care was provided as routine care according to the patients' wishes. Twelve symptoms were measured using 9 items (numbers 1-9) from the Edmonton Symptom Assessment System-Revised Japanese version and 3 items from the Cancer Fatigue Scale. Outcomes were compared between bathing days and nonbathing days (control) and between before and after bathing. Of the 57 bathers, data were available for both bathing days and nonbathing days for 42 bathers. In the comparison between bathing and nonbathing days, tiredness was significantly improved (effect size [ES], 0.35; P = .02). On the basis of the pre-post bathing comparison, 6 symptoms, namely, tiredness (ES, 0.40; P < .01), lack of appetite (ES, 0.36; P = .01), decreased well-being (ES, 0.33; P = .01), anxiety (ES, 0.36; P = .01), pain (ES, 0.31; P = .02), and depression (ES, 0.30; P = .02), were significantly improved. Bathing in a tub effectively improves tiredness and might be effective for distressing symptoms in end-of-life cancer 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.063
GPT teacher head0.469
Teacher spread0.406 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Hospice and Palliative NursingSame topicTherapeutic Uses of Natural ElementsFrench-language works237,207