Patient safety in the medication system: nurses evaluation in a teaching hospital
Bibliographic record
Abstract
This study aimed to identify and describe the factors related to Patient Safety in a medication system according to the nurses analysis in a teaching hospital from the photographic analysis method. This was a cross-sectional, descriptive study with mixed approach in a teaching hospital in Rio Grande do Norte. The population consisted of 42 nurses from inpatient units, of which 34 composed the study sample. As eligibility criteria, we defined nurses from public service and nurses who agreed to participate. Ethical determinations were observed, the study was submitted to the Ethics and Research of the University Hospital Onofre Lopes, obtaining the assent with ethical assessment certificate (CAAE 0098.0.051.294-11). For data collection, we used the photographic method (Photographic Analysis Technique) by Patricia Marck (Canada). It was developed in two phases: at first, we randomly captured photos from the medication system, resulting in 282 images; then we selected/processed the photographs, which were reduced to 10 images in Microsoft Excel 2010; in the second phase, the nurses answered the questionnaire divided into socio-professional profile and Digital Photography Scoring Tool (questions a and b ). For analysis of the question a , we used the content analysis technique, and for b , we used the Statistical Package for the Social Sciences 20.0 (temporary license). The socio-professional profile revealed the predominance of females; age group 34-43 years; professionals with specialization; 10-18 years of length of service; and nurses working exclusively in the hospital and who know the Patient Safety. The photographic analysis in relation to Patient Safety resulted in specific categories for each stage of the medication system. Regarding disposal, we identified Proper verification ; Improper verification ; Correct identification ; Disposal in single doses ; and Improper Environment , with predominance of that last category. As for storage: Proper storage ; Improper storage ; Risk of exchange/disappearance ; and Poor hygiene , with special reference to improper storage. In preparation: Risk of exchanging medication/patient ; Inappropriate physical space ; and Inadequate 9 preparation of controlled drugs , highlighting the first category. In drug administration: Lack of Personal Protective Equipment ; Use of Personal Protective Equipment ; Improper administration technique ; Proper administration technique ; Correct drug identification ; Incorrect drug identification ; and Peripheral venous access without identification . From the safety assessment of 10 photographs, by adapting the scores (1-10) to the Likert Scale, we identified three Totally Unsafe (Level 1), three Unsafe (Level 2), three Partially Safe (Level 3), one Safe (Level 4), and no photograph considered Totally Safe. This study identified the prevalence of unsafety in the medication system in the nurses opinion. We were also able to understand that, although nurses identify safety aspects, the most prevalent categories characterize an unsafe assessment. Nursing needs to reflect on its practice, identifying gaps in the medication system in order to achieve a proper and safe care
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".