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Record W2765252346 · doi:10.5430/jha.v6n6p15

Patients’ perception of safety in four hospitals in Tamaulipas, Mexico

2017· article· en· W2765252346 on OpenAlexvenueno aff
Concepción Meléndez Méndez, Rosalinda Garza Hernández, Juana Fernanda González Salinas, Socorro Rangel Torres, Gloria Acevedo Porras, Hortensia Castañeda-Hidalgo

Bibliographic record

VenueJournal of Hospital Administration · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePatient safetyPerceptionMedication errorHealth careComplicationFamily medicineSurgeryPsychology

Abstract

fetched live from OpenAlex

Objective: To determine the perceived patient safety related to health care during hospitalization. To identify the number of patients who report having suffered a clinical error and describe the patients’ experience with the clinical error.Methods: A cross-sectional descriptive study performed of patients who were hospitalized between August-November 2013 in four second-level hospitals.Results: A total of 631 patients were surveyed. Regarding the errors suffered during the hospitalization, 7.9% of the patients reported having suffered a complication, 7.9% reported having an infection, 5.2% had an allergic reaction to medication and 5.1% had to undergo a second surgery. Regarding the patients’ responses about the experience with the error, only 4.8% of the patients reported having had experiencing clinical error in their management, 1.9% mentioned that they fully agreed that the error was solved quickly, 2.5% that the error was solved satisfactorily and 3.3% patients disagreed as they were not informed if steps would be taken to prevent the error from recurring.Conclusions: To address safety culture in the hope of improving patient safety will continue to motivate nurse researchers and managers thus more research about patient perception is needed.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.008
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.382
Teacher spread0.347 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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