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Record W2154524389 · doi:10.1177/0269216310395985

What does the answer mean? A qualitative study of how palliative cancer patients interpret and respond to the Edmonton Symptom Assessment System

2011· article· en· W2154524389 on OpenAlexaboutno aff
Irmelin Bergh, Ingela Lundin Kvalem, Nina Aass, Marianne Jensen Hjermstad

Bibliographic record

VenuePalliative Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePalliative careCancerQualitative researchFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

The Edmonton Symptom Assessment System (ESAS) is a well-known self-reporting tool for symptom assessment in palliative care. Research has shown that patients experience difficulties in the scoring and interpretation, which may lead to suboptimal treatment. The aims were to examine how palliative care cancer patients interpreted and responded to the ESAS. Eleven patients (3 F/8 M), median age 65 (34-95) with mixed diagnoses were interviewed by means of cognitive interviewing, immediately after having completed the ESAS. The highest mean scores were found with tiredness (6.3) and oral dryness (5.7). The results showed that sources of error were related to interpretation of symptoms and differences in the understanding and use of the response format. The depression and anxiety symptoms were perceived as difficult to interpret, while the appetite item was particularly prone to misunderstandings. Contextual factors, such as mood and time of the day, influenced the answers. Lack of information and feedback from staff influenced the scores. Some patients stated that they scored at random because they did not understand why and how the ESAS was used. The patients' interpretation must be considered in order to minimize errors. The ESAS should always be reviewed with the patients after completion to improve symptom management, thereby strengthening the usability of the ESAS.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.050
GPT teacher head0.381
Teacher spread0.331 · 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 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

Citations32
Published2011
Admission routes1
Has abstractyes

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