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Record W2945505663 · doi:10.1590/1518-8345.1610.2847

Knowledge in palliative care of nursing professionals at a Spanish hospital

2017· article· en· W2945505663 on OpenAlexfundno aff
Elena Chover‐Sierra, Antonio Martínez‐Sabater, Yolanda Raquel Lapeña-Moñux

Bibliographic record

VenueRevista Latino-Americana de Enfermagem · 2017
Typearticle
Languageen
FieldMedicine
TopicPalliative and Oncologic Care
Canadian institutionsnot available
FundersUniversitat de ValènciaUniversity of Ottawa
KeywordsPalliative careNursingMedicineBivariate analysisDescriptive statisticsFamily medicineDescriptive researchNurse educationPsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: to determine the level of knowledge in palliative care of nursing staff at a Spanish tertiary care hospital. METHOD: descriptive, cross-sectional study. Data were collected about the results of the Spanish version of the Palliative Care Quiz for Nurses (PCQN), sociodemographic aspects, education level and experience in the field of palliative care. Univariate and bivariate descriptive analysis was applied. Statistical significance was set at p < 0.05 in all cases. RESULTS: 159 professionals participated (mean age 39.51 years ± 10.25, with 13.96 years ± 10.79 of professional experience) 54.7% possessed experience in palliative care and 64.2% educational background (mainly basic education). The mean percentage of hits on the quiz was 54%, with statistically significant differences in function of the participants' education and experience in palliative care. CONCLUSIONS: although the participants show sufficient knowledge on palliative care, they would benefit from a specific training program, in function of the mistaken concepts identified through the quiz, which showed to be a useful tool to diagnose professionals' educational needs in palliative 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.090
GPT teacher head0.445
Teacher spread0.355 · 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 source (direct Gemma or distilled Codex), 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

Citations61
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueRevista Latino-Americana de EnfermagemSame topicPalliative and Oncologic CareFrench-language works237,207