MétaCan
Menu
Back to cohort

Assistência segura ao paciente no preparo e administração de medicamentos

2018· article· pt· W2768886070 on OpenAlexaff
Eliana Ofélia Llapa-Rodríguez, Luciana de Santana Lôbo Silva, Max Oliveira Menezes, Júlian Katrin Albuquerque de Oliveira, Leanne M. Currie

Bibliographic record

VenueRevista gaúcha de enfermagem · 2018
Typearticle
Languagept
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

OBJECTIVE To evaluate the compliance with the assistance and the adhesion of nursing professionals for the safe administration of drugs in an Intensive Care Unit of a public hospital in Sergipe, Brazil. METHOD Quantitative, descriptive and cross-sectional study carried out by direct non-participant observation. Data collection performed in 2015. Non-probabilistic sample, for convenience, consisting in the observation of 557 doses of prepared and administered drugs. For data analysis, descriptive statistics were used for data analysis. RESULTS The items classified as safe care were: correct via (85.7%) and correct form (100%). The items classified as undesirable care were: correct patient (33.3%), correct medication (66.67%), correct dose (50%), correct register (33.33%), correct orientation (0%), and correct time (50%). CONCLUSION The practice was evaluated according to Carte's positivity index as undesirable care, considering that six of the eight items had low adhesion. The found weaknesses compromised the whole process of drug administration.

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)0.002
metaresearch head score (Gemma)0.019
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.099
GPT teacher head0.451
Teacher spread0.352 · 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

Citations53
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueRevista gaúcha de enfermagemSame topicPatient Safety and Medication ErrorsFrench-language works237,207