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Cancer Nursing Research Priorities

2000· article· en· W2329786989 on OpenAlexaboutno aff
Tone Rustøen, Tore Kr. Schjølberg

Bibliographic record

VenueCancer Nursing · 2000
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsNorwegianNursingMedicinePsychosocialNursing researchFamily medicineBurnoutPsychiatry

Abstract

fetched live from OpenAlex

The purpose of the study was to determine research priorities among Norwegian nurses in cancer care, and to investigate implications that these priorities might have for future planning of nursing research. Differences between specialists in cancer nursing and other nurses working in cancer care, and between the current results and earlier findings in this area also were evaluated. Half the members of The Norwegian Society of Nurses in Cancer Care (n = 197) were mailed a questionnaire used in a similar Canadian study. The nurses were asked to select the five topics they perceived as most important from a list of 80 items, and to rank them in order of research priority. The response rate was 43% (197/464), and 75 respondents were specialists in cancer nursing. Quality of life was given the highest research priority in the total sample. Psychosocial support/counseling, communication between patient and nurse, patient participation in decision making, nurse burnout, and ethics also were ranked highly. In contrast to the others, cancer nursing specialists ranked ethics as their number one priority. Except for symptom management, the priorities given in Norway and other Western countries were found to be similar. These results might suggest topics for future research tailored to the needs of cancer nursing.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score1.000

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)0.0010.000

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 head0.311
GPT teacher head0.574
Teacher spread0.263 · 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 designOther design
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

Citations17
Published2000
Admission routes1
Has abstractyes

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