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Record W3014067009 · doi:10.1097/ncc.0000000000000814

Too Much Cancer Care?

2020· article· en· W3014067009 on OpenAlexaff
Moriah Ellen, Saritte Perlman, Ruth Shach

Bibliographic record

VenueCancer Nursing · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsHamilton Health SciencesMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineMultidisciplinary approachHarmPsychological interventionNursingHealth careTeamworkPerceptionFamily medicinePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: "Unnecessary use of health services" refers to care that does not add value for patients and can lead to physical, emotional, and economical harm. High rates of overuse have been reported within oncology, and patients experience its consequences. OBJECTIVE: The aim of this study was to explore perceptions and experiences of oncology nurses regarding unnecessary use of oncology services. METHODS: In-depth, semistructured interviews were conducted with a convenience sample of 20 oncology nurses currently practicing in Israel. Interviews were recorded, transcribed, and analyzed thematically. RESULTS: Themes included perceptions of unnecessary use of health services in cancer (causes and effects of unnecessary use, current and proposed solutions) and negative effects of unnecessary cancer care on patients, families, providers, and the system, including decreased quality of life, increased suffering, and emotional effects on patients and families. Causes were seen on provider, family, and patient levels, such as difficulty for providers to "give up," lack of registered nurses' authority, and family and patient demands. Multidisciplinary care provision, nurses' role, and the patient-provider relationship were seen as existing facilitators minimizing unnecessary use. Future improvement can be achieved by strengthening relationships, providing support to healthcare providers, and improving communication. CONCLUSIONS: Nurses perceive unnecessary use of health services as a result of multiple, interlinked and complex causes, but few targeted interventions exist. Future research should explore quantifying unnecessary use to determine an accurate representation of the issue. IMPLICATIONS FOR PRACTICE: Solutions should include engaging patients and families, involving nurses, and fostering multidisciplinary collaborative teamwork to positively affect care and treatment decision-making processes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.784
GPT teacher head0.645
Teacher spread0.139 · 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 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

Citations6
Published2020
Admission routes1
Has abstractyes

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