Facteurs liés au signalement des évènements indésirables associés aux soins dans un hôpital tunisien
Bibliographic record
Abstract
INTRODUCTION: We wish to integrate an adverse events reporting system in a Tunisian University Hospital. However, before the implantation of this system, it is important to identify the factors that may influence the reporting, so it is primordial to conduct a study which aims to determine influencing factors of adverse events reporting according to the perception of health care professionals. METHOD: A cross-sectional descriptive study was conducted between July and September 2014, using a questionnaire which was developed in the light of Reason’s works on safety culture (1990; 1997), and the Pffeifer, Manser and Wahner (2010) model of influencing factors of adverse events reporting. This questionnaire was self-administered to 46 physicians, 21 health technicians, 65 nurses and 18 practical nurses working in a Tunisian Hospital. Data analysis was conducted using SPSS. RESULTS: The main obstacles identified were: lack of staff training (78.7%) and lack of precision on the types of events reported (76.7%). However, the three main facilitators are the establishment of a safety culture (88%), the commitment of decision makers in the safety culture (81.3%) and the absence of punishment (78, 7%). CONCLUSION: A policy and managerial consideration of the main factors influencing reporting of adverse events, as well as suggestions from health professionals, is necessary to ensure a good adoption of the reporting system by healthcare institutions in Tunisia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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; both teacher heads agree on what is shown here.
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".