Can the craze for patient safety hinder a more holistic care of the person in the health care system?
Bibliographic record
Abstract
Background and objective: Since the publication of a report by the Institute of Medicine on the mortality associated with adverse events in the hospital, patient safety has become one of the essential objectives of the health care system. However, this movement tends to obscure the fundamental link between safety and quality of care in the health system. The study was aimed to demonstrate that the only focus on patient safety concept overshadow the more holistic care of the person and the population in the health care system.Methods: Documentary research in the Pubmed database and the Google Scholar search engine, from 1999 to 2017.Results and conclusion: Highly targeted safety research without addressing quality at first can only be a long-term panacea for current health policies. For cause, a one-way look at patient safety could lead to significant impacts at the population level. In order to get out of this craze, health system decision-makers would benefit from supporting clinical governance advocating humanistic and holistic strategies for interventions, engaging in a process of continuous improvement of the Quality of care more profitable in the long term. In order to overcome this craze, health system decision-makers would benefit from supporting clinical governance that advocates humanistic and holistic strategies for interventions, by engaging in a process of continuous improvement in the quality of care that is most beneficial in the long term. This posture is similar to Caring's well-known nursing model.
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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 | 0.040 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".