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Record W4250485961 · doi:10.1108/lhs-07-2018-083

Guest editorial

2018· editorial· en· W4250485961 on OpenAlexaffabout
Madhav N. Sinha

Bibliographic record

VenueLeadership in health services · 2018
Typeeditorial
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsMedicinePolitical sciencePsychology

Abstract

fetched live from OpenAlex

This Special Issue of Leadership in Health Services Journal contains selected papers that were presented at the 9th Canadian Quality Congress held at the University of Toronto, in Toronto, Ontario, Canada, September 7-8, 2017.The theme of the congress was Quality and Innovation in the 21st Century: Challenges and Opportunities.The special issue contains papers on many interesting topics.Quality and innovation is the central theme.It has a mixture of theoretical, practical and those that are rooted in conceptual framework thinking for improving health-care organizations.Leadership is at the core of all issues discussed.The papers represent very timely topics that are currently hotly debated and discussed throughout North America and in other parts of the world, that is, the quality and patient satisfaction must improve and costs must go down.The first paper discusses how leveraging supply chain infrastructure can advance patient safety in community-based health-care settings.The authors argue that while the majority of patient safety studies in Canada and abroad have focused on safety in hospital settings, deaths and harm experienced by patients in the community settings (home care, long-term care, complex care and rehabilitation) are less well understood.The authors suggest that improving system infrastructure would reduce the occurrence of adverse events.According to them, the visibility across the continuum of care holds the potential to transform health care in Canada rather than a fragmented system where information is inadequately captured and transferred from provider to provider, to a system that would provide complete, accurate and up-to-date information of patient care, procedures, medications and outcomes to inform the best and safest care possible.The second paper presents a comprehensive approach for studying organizational culture using "soft measures" to facilitate sustainable quality development.The author's work consists of a number of different methods to collect soft data that influences the culture and the leadership within three organizations that were shown statistically significant with positive changes in organizational work culture only after a short period of one year.The next paper is about improving patient flow in an emergency department of hospital using lean methodologies.The emergency department overcrowding (EDOC) and increased length of stay (LOS) have been key global issues for more than 20 years with many serious repercussions.Guided by the Lean management concepts, the author proposes solutions that fall under three themes: ensuring effective triaging of all patients, reducing the total number of patients referred to observation room and reducing maximum LOS and wait times in observation room.The solutions address the vital few causes of the EDOC and prolonged EDLOS that are critical to solving ED problems.The fourth paper is about developing a model for measuring effectiveness of quality management practices in health-care organizations.Health care is the example where the needs of potential clients often exceeds the capabilities of the service delivery system.The I am grateful to my team of volunteers, Editorial Review and Technical Program Committee members for their dedication and support.Sincere thanks are extended to Ms Lorraine Connolly, the content editor of Leadership in Health Services Journal along with Ms Sharon Parkinson and her technical staff at Emerald Publishing Group, Jennifer Bowerman, the editor and Jo Lamb-

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.739
Threshold uncertainty score0.874

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0070.004
Open science0.0020.002
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.2610.139

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.

Opus teacher head0.170
GPT teacher head0.450
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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