Critical evaluation of transforming care at the bedside application in a multi-model nursing practice: A reflective review
Bibliographic record
Abstract
It will soon be a decade since Transforming Care at the Bedside (TCAB) initiated by the Institute of Healthcare Improvement (IHI) was introduced. This noble idea has had great successes and led to improvements in patient safety. Some institutions have successfully implemented and realized the benefits. We have conducted a reflective review of the possible challenges that may hinder other institutions from successfully implementing this initiative and suggest some action plans that may deliver a better result. Our review broadly examines the main nursing models in relation to patient care quality improvement, that is American and British models and we base our arguments on our experiences in Australia, the United Kingdom, Ireland and in Saudi Arabia. Where the latter’s healthcare practices are a prototype of the American model, mainly because in America, the healthcare is primarily private and has to often cope with care related litigation, contrary to Saudi Arabia where the government offers free healthcare to all its citizens and also, the citizens have the privilege of opting to seek care in other organizational facilities in which they are eligible. Our review points out that, though all other aforementioned countries have taken steps to incorporate TCAB, it is a new initiative being introduced into one of Saudi Arabia’s top healthcare organizations. Being participants in this organization which has recently moved to “implementing” TCAB, our study is focused on reviewing the systems readiness in implementing this initiative, with special focus on system structure and planning. We also discuss how achievement of success in implementing TCAB and all other quality improvement projects require system and needs analysis and thorough preparedness of all relevant players. The discussion will carefully consider and deliberate on TCAB’s core agendas but can be extrapolated to all other safety and quality projects undertaken within a healthcare system.
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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 | 0.160 | 0.371 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".