In response to: Tourangeau A.E., Doran D.M., McGillis Hall L., O'Brien Pallas L., Pringle D., Tu J.V. & Cranley L.A. (2007) Impact of hospital nursing care on 30‐day mortality for acute medical patients. <i>Journal of Advanced Nursing</i> 57(1), 32–44.
Bibliographic record
Abstract
I read with interest Tourangeau et al.’s (2007) article published in the Journal of Advanced Nursing, 57(1), 32–44. As an acute care clinician, baccalaureate nurse educator, programme co-ordinator and chair of a provincial practical nursing program approval committee, I am particularly interested in research that explores the relationship between hospital structures and patient outcomes. These studies are particularly relevant in Ontario, Canada where legislation (Canadian Nurses Association 2004) has mandated the baccalaureate (BScN) credential as entry to practise for Registered Nurses (RNs) and a 2-year college diploma for Registered Practical Nurses (RPNs). The conduct of research into skill mix and educational preparation of the healthcare team and the relationship of these factors to patient outcomes has the potential to advance the nursing profession as well as contributing to the ultimate goal of effective and safe care for patients. In addition, Tourangeau et al.’s study, however well designed and conducted, may negatively impact on the progress of the nursing profession if caution is not exercised in the interpretation of the results. Specifically, if readers make sweeping generalizations, as opposed to careful analysis of contextual relevance, there is great potential for fallacies to occur because of inadequately reasoned evidence. Tourangeau et al.’s (2007) study tested an elegant model that hypothesized hospital structures and processes of care, and their effect on patient mortality in acute medical units. Of particular interest to me are the authors’ comments on the variables of educational preparation and skill-mix of the healthcare team, and their impact on patient mortality. In their discussion, the authors assert that a higher proportion of RNs in the staffing mix – and a higher proportion of BScN (prepared RNs in the healthcare team) – is associated with lower 30-day mortality rates among medical patients. The results presented in this study – namely the value of the BScN credential as entry-to-practice for RNs and increased proportion of RNs in the healthcare team – are well supported and are not in dispute. The results presented, however, do not address the human resources challenges for nursing in today's healthcare systems and practice environments, nor the recent changes in educational preparation of members of the healthcare team. Extreme caution must be exercised when considering application of these findings to the current healthcare delivery system in Ontario, and especially when considering the policy implications. Tourangeau et al.’s (2007) interpretations and conclusions are based on the data collected in nursing care environments, and within a healthcare system, that no longer exhibits similarity with the assumptions underpinning the study. Specifically, at the time the data were collected (2002–2003), Ontario was in the midst of educational reform with regard to the RN and RPN programmes [College of Nurses of Ontario (CNO) 2007] and also the personal support worker (PSW) programmes. These programme reforms have been implemented and the graduates are beginning to alter the composition of the healthcare team. Further, an enhanced scope of practice for the RPN has been legislated and implemented in Ontario (College of Nurses of Ontario 2005) and new practical nursing graduates would be as different from their former counterparts as a diploma-prepared RN would be from a BScN graduate. Thus, this study represents conditions at a point in time that have become significantly altered, rendering the research findings inappropriate for comparison to the newly emerging healthcare system that includes better educated healthcare personnel at all levels. As pointed out by Tourangeau et al. (2007), the data obtained in this study explained 45% of the variance in 30-day mortality rates in medical patients. I agree with their assertion that further research is required to investigate the contextual factors that might contribute to the remainder. Registered and non-registered healthcare personnel are being prepared in markedly different ways today. In Ontario, collaborative BScN nursing education programmes that foster teamwork and interprofessional health education, and RPN programmes that have a curricular emphasis on leadership, consultative-collaborative practice, and critical thinking skills, are producing graduates capable of contributing to a markedly different healthcare team, both in terms of skill-mix and education preparation, than we previously experienced. The effects of the new healthcare graduates, and the more effective healthcare team on patient outcomes, would require a longitudinal study with effective control/intervention group analysis to avoid errors that might arise from an evaluation at a single point in time. As such, there is a vital role for research that explores the effects of healthcare delivery models which utilize today's healthcare team members (RN, RPN and PSW) to their full scope of practice. How exciting to think of studies that might demonstrate the effectiveness of alternate models for safe delivery of patient care at a time when we are facing the most profound shortage of healthcare personnel in recent history. Tourangeau et al. (2007) have provided us with a significant piece of research which nurse scientists can, and must, build upon in search of healthcare delivery models which optimize patient care in the face of the projected severe nursing shortage. We need to continue to explore nursing care delivery models that support the full utilization of skills of all categories of healthcare personnel. Implementation of a system that utilizes full scope of practice among all registered and non-registered staff, in a supportive and collaborative environment, is a concept worthy of acceptance, implementation and evaluation. We need to be critical of the context in which we might apply the conclusions offered by Tourangeau et al., lest we advocate a model that may not be possible to justify or sustain in the face of a human resource crisis. Applying the findings to support a political or ideological position could detract from the historical progress the nursing profession has made in recent years, and limit the potential to realize a care delivery model that optimizes patient safety in the emerging human resource shortage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".