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Record W4414260363 · doi:10.58840/05xa5m93

Leadership Ineffectiveness In Executing Policies in Healthcare

2025· article· en· W4414260363 on OpenAlexaboutno aff
A. Sheik Abdullah

Bibliographic record

VenueOTS Canadian Journal · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Leadership and Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careHierarchyQuality (philosophy)BurnoutQualitative researchOrganizational cultureFocus groupTransformational leadership

Abstract

fetched live from OpenAlex

The book explores the critical issue of ineffective leadership in healthcare policy implementation, with a particular focus on Vancouver’s healthcare sector. Using a qualitative single case study design, it examines how leadership shortcomings contribute to widespread challenges such as employee burnout, high turnover, and financial instability. Drawing upon in-depth interviews with healthcare professionals, the study uncovers systemic gaps and connects them to broader organizational outcomes. The analysis is enriched through conceptual frameworks such as Maslow’s Hierarchy of Needs and motivation theories, offering a structured understanding of the factors driving employee dissatisfaction and disengagement. One of the central themes of the book is the inability of healthcare leaders to effectively execute policies intended to improve retention and reduce burnout. This failure often results in workplace stress, disengagement, and a decline in the quality of patient care. Burnout emerges as a pervasive issue, with more than half of healthcare staff experiencing emotional exhaustion. High turnover rates not only disrupt patient care but also impose staggering financial costs, with the replacement of a single nurse reaching up to $88,000. Collectively, these outcomes threaten both the stability and sustainability of healthcare organizations.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.247
Teacher spread0.209 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2025
Admission routes1
Has abstractyes

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