Repercusión de los eventos adversos en los profesionales sanitarios: estudio sobre las segundas víctimas
Bibliographic record
Abstract
espanolObjetivo Consensuar recomendaciones para paliar los efectos de eventos adversos (EA) en las segundas victimas. Material y metodo Mediante un metodo de investigacion cualitativa se establecieron recomendaciones de expertos sobre: apoyo emocional a segundas victimas, asesoria legal y manejo de la comunicacion institucional cuando se produce un evento adverso. Se utilizaron grabaciones de las sesiones, clasificando las ideas en categorias mutuamente excluyentes, y se considero puntuacion media y el grado de acuerdo (coeficiente variacion). Resultados Los profesionales sanitarios implicados en un error que ocasiona un EA con consecuencias graves sufren graves problemas emocionales. Ante un EA se recomendo transparencia informativa, pedir disculpas y reparar el dano. Existio acuerdo en la necesidad de apoyar a los profesionales implicados e informar a los pacientes y al resto de profesionales de la organizacion. Conclusion Se presentan recomendaciones consensuadas sobre la comunicacion al paciente que ha sufrido un error clinico, afrontar el error por parte de los profesionales y proteger la reputacion profesional y social de profesionales e instituciones EnglishObjective: To establish consensus regarding the recommendations to palliate the effects of adverse events (AEs) in the second victims. Methods: A qualitative study was made to establish expert recommendations referred to: emotional support for second victims, legal counseling, and management of institutional communication when an adverse event occurs. In addition, recordings of the sessions were used, classifying the ideas into mutually excluding catego- ries. The mean score and degree of agreement (coefficient of variation) were considered. Results: The healthcare professionals implicated in an error resulting in an adverse event with serious conse- quences suffer important emotional problems. In the event of an adverse event, the recommendations were transparency of information, the offering of sincere apologies, and repair of the damage caused. There was agreement over the need to support the implicated healthcare professionals and to inform the patients and the rest of the professionals of the organization. Conclusion: Consensus-based recommendations are presented referred to informing the patient that there has been a clinical error, coping with the error on the part of the healthcare professionals, and safeguarding the professional and social reputation of both the professionals and the institutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".