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Record W2219282577 · doi:10.3390/healthcare4010003

A Tale of Two Sites: Lessons on Leadership from the Implementation of a Long-term Care Delivery Model (CDM) in Western Canada

2016· article· en· W2219282577 on OpenAlexafffundabout
Amy Cox, Ruth Kampen, Karen Kobayashi, Heather Cook, Deanne Taylor, Gina Gaspard

Bibliographic record

VenueHealthcare · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsInterior HealthUniversity of Victoria
FundersMichael Smith Health Research BC
KeywordsLeadership styleNursingContext (archaeology)Health careJob satisfactionServant leadershipPsychologyLong-term carePublic relationsWork (physics)MedicinePolitical scienceEngineering

Abstract

fetched live from OpenAlex

Residential, long-term care serves vulnerable older adults in a facility-based environment. A new care delivery model (CDM) designed to promote more equitable care for residents was implemented in a health region in Western Canada. Leaders and managers faced challenges in implementing this model alongside other concurrent changes. This paper explores the question: How did leadership style influence team functioning with the implementation of the CDM? Qualitative data from interviews with leadership personnel (directors and managers, residential care coordinators and clinical nurse educators), and direct care staff (registered nurses, licensed practical nurses, health care aides, and allied health therapists), working in two different facilities comprise the main sources of data for this study. The findings reveal that leaders with a servant leadership style were better able to create and sustain the conditions to support successful model implementation and higher team functioning, compared to a facility in which the leadership style was less inclusive and proactive, and more resistant to the change. Consequently, staff at the second facility experienced a greater sense of overload with the implementation of the CDM. This study concludes that strong leadership is key to facilitating team work and job satisfaction in a context of change.

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 score0.168
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.162
GPT teacher head0.448
Teacher spread0.286 · 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

Citations21
Published2016
Admission routes3
Has abstractyes

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