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Record W2119182769 · doi:10.12927/hcq.2011.22160

Enabling Transformational Change: The Ontario Shores Experience

2011· article· en· W2119182769 on OpenAlexaffabout
Glenna Raymond, Mark Walton

Bibliographic record

VenueHealthcare Quarterly · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsOntario Shores Centre for Mental Health Sciences
Fundersnot available
KeywordsTransformational leadershipMindsetAgency (philosophy)Public relationsAccountabilityOrganizational cultureMental healthGovernment (linguistics)Health careShoreOrganisational changeAuditNursingBusinessPolitical scienceMedicineSociology

Abstract

fetched live from OpenAlex

This case study outlines key considerations for healthcare organizations experiencing significant transformational change, based on the experience of Ontario Shores Centre for Mental Health Sciences (Ontario Shores), formerly Whitby Mental Health Centre. Significant systemic change requires specific and intentional efforts from the leaders tasked with carrying out transformational activities. This article presents the perspectives of leaders involved in the transformation of Ontario Shores as it moved from a government-based agency to a stand-alone specialty psychiatric hospital in 2006. During this time, several conventional strategies were employed to manage the transition, but various critical approaches also emerged that assisted the organization to effect significant change and achieve marked improvements over key evaluation metrics. These critical strategies included maximizing the distinct and collective roles of governance and leadership; balancing strategy and action through a culture of accountability; leveraging strategic communication opportunities; and shifting the organizational mindset.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.545

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0410.018
Scholarly communication0.0060.003
Open science0.0020.010
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.312
GPT teacher head0.444
Teacher spread0.132 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2011
Admission routes2
Has abstractyes

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