Bibliographic record
Abstract
Quarterly is published in conjunction with National Nursing Week, thus a major focus of the issue centers on the topic of nursing best practices.The Registered Nurses Association of Ontario (RNAO) has made the development of nursing best practice guidelines a priority, and we are very pleased to report on this excellent work as it is implemented in several organizations.The relationship between nursing satisfaction and high-quality patient care has been documented in study after study, both here in Canada and elsewhere.There is also a significant body of evidence to show that nursing satisfaction is directly related to an organization's ability to recruit and retain the most capable personnel.Providing healthcare organizations and their nurses with tools, such as these evidence-based clinical guidelines, will heighten nurses' ability to provide quality patient care and ultimately increase their satisfaction.To introduce the nursing best practices, we have comments from Judith Shamian, Executive Director of Nursing Policy for Health Canada, and Mary Beth Valentine, Chief Nursing Officer for the Province of Ontario.Shamian urges vigilance in the implementation of best practices so that they remain current and relevant.Valentine cites the project as an example of successful partnering between the nursing profession, government and healthcare organizations.The first feature article in the series, by Grinspun, Virani and Bajnok, builds on an article published in the winter issue of Hospital Quarterly (Vol.5 No. 2) and provides an overview of the best practice guidelines as tools for organizational change.Next are examples of how two large hospital corporations are moving ahead with implementing and evaluating the new guidelines.Special thanks to the Humber River Regional Hospital and the University Health Network for taking leadership here and for sharing their "lessons learned" with us.Waiting lists are another topic of considerable debate.Frequently in Canada, we hear people complain about the length of time they must wait before they receive a relatively minor diagnostic or treatment procedure.Health policy experts from other countries criticize the Canadian healthcare system because of our reliance on waiting lists, which they see as a means to "ration" health services.Analysis of the experiences of other countries shows that waiting lists are a complex problem that is prone to media scrutiny if the public perceives the balance between access and availability of services to be in jeopardy.Waiting lists, and how they are handled, evoke emotional outbursts from physicians and policy-makers, to the media and the public, but as yet we have not been able to come up with an acceptable solution.In this issue of Hospital Quarterly, we report on three studies that looked closely at the challenges inherent in waiting lists.The Western Canada Waiting List Project was funded in part by Health Canada's Health Transition Fund.It was an ambitious analysis of the problems associated with waiting lists for elective procedures in the four western provinces.Input for the study was extensive, with participation from provincial governments, regional health authorities and other healthcare providers.The investigators also solicited feedback from the public, who seemed somewhat amazed that the myriad of healthcare providers and policy-makers had not been able to develop a comprehensive, fair and ethical system for setting priorities for waiting lists.In Ontario, the Joint Policy Planning Committee has worked extensively to provide information to the Ontario Hospital Association and the Ontario Ministry of Health and Long-Term Care to enhance understanding of waiting list issues.Peter Glynn, who is an experienced policy-maker at provincial and federal levels and who has also served as the CEO of one of Ontario's academic health sciences centers, recently completed a study of the waiting list situation in Saskatchewan and the highlights of that study are reported here.Also in this issue is a timely article on private-sector marketing strategies by Keenleyside.It offers a range of new ideas for communicating with organizational stakeholders.We include an in-depth review of an evidence-based approach to building healthier workplaces through collaboration by Yassi and colleagues in Vancouver.A profile of Dr Noni MacDonald, Dean of Medicine at Dalhousie University, provides considerable food for thought on a range of topics including altering of healthcare systems and the need for humanity in medicine.We are also pleased to present topical columns from ICES, POLLARA and the The Berger Population Health Monitor.I would also recommend readers review the two latest editions of HealthcarePapers.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.257 | 0.168 |
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".