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Record W2101526846 · doi:10.12968/ijpn.2013.19.12.593

Nurses' experiences caring for patients and families dealing with malignant bowel obstruction

2013· article· en· W2101526846 on OpenAlexaff
Patricia Daines, Kalli Stilos, Shari Moura, Margaret I. Fitch, Alison McAndrew, Ashlinder Gill, Frances C. Wright

Bibliographic record

VenueInternational Journal of Palliative Nursing · 2013
Typearticle
Languageen
FieldMedicine
TopicIntestinal and Peritoneal Adhesions
Canadian institutionsSunnybrook Health Science CentreHealth Sciences CentreInstitute of Health Services and Policy ResearchUniversity of TorontoWomen's College Hospital
Fundersnot available
KeywordsBowel obstructionMedicineNursingPalliative careIntensive care medicineGeneral surgerySurgery

Abstract

fetched live from OpenAlex

Malignant bowel obstruction (MBO) is a well-recognised complication of advanced abdominal and pelvic cancers. Often surgical intervention is not feasible, resulting in complex symptoms and an unpredictable course. Although symptom management is a crucial part of nursing care, psychosocial and emotional issues frequently emerge for patients and families. This qualitative study explored the perspectives of nurses from a palliative care unit, in-patient acute care oncology units, ambulatory cancer setting, and the community on their experiences of caring for patients with MBO and their families. Six individual interviews and two focus groups were conducted. Eight overarching messages were identified related to nurses' experiences. Highlights include aspects of patients' and families' emotional distress, and the nurse-patient relationship in relieving suffering. Nurses have an important and privileged role that involves identifying MBO signs and symptoms, having knowledge of treatment and symptom management options, and helping patients transition from a curative to a palliative philosophy of care.

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 score0.137
Threshold uncertainty score0.254

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 integrity0.0000.000
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 head0.021
GPT teacher head0.320
Teacher spread0.298 · 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

Citations15
Published2013
Admission routes1
Has abstractyes

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