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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".