Bibliographic record
Abstract
Background and Objectives: Healthcare is one of the important sectors where the level of risk is so high, hence, the errors and mistakes that occur within this sector and cause failures in the system are also very important.In this context, one of the important errors is the human error that its examination, within the framework of healthcare, infection control and dentistry, can prevent many adverse and unpleasant consequences.The present study has been conducted to identify the causes of human errors in infection control practices such as hand hygiene and the use of personal protective equipment (PPE).Methodology: Present study has been conducted in a specialized dental center.Data collection is performed by interviewing dental specialists and using analyst's reports and observations regarding those tasks of the dentist that are related to infection control practices such as the use of personal protection equipment and hand hygiene.After identifying human errors and collecting the necessary data, they were analyzed by using SHERPA technique and then, controlling strategies were presented.Findings: Considering the causes of errors, the following results were obtained: forgetfulness and distraction 34%, out-of-work fatigue 3%, inappropriateness of conditions and equipment 4/5%, lack of awareness 17%, forgetting the items taught during the training courses 20% and ineffectiveness of training materials 21.5%.Conclusion: The identified errors, results obtained from the evaluation of the causes of errors and the strategies presented to control them, all, indicate that SHERPA is a suitable and applicable technique in the field of infection control in dentistry, as it has been proved to be useful in studying other fields related to the healthcare and recognizing the root causes of identified errors through this technique, can play an important role in determining the strategies, the way of implementing and programming them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.957 | 0.955 |
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; the direct Gemma label and the distilled Codex classifier 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".