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Record W3047493344 · doi:10.12927/hcq.2020.26278

Building and Sustaining a Culture of Innovation in an Academic Health Centre

2020· article· en· W3047493344 on OpenAlexaffvenueabout
Rukhsana Merkand, Doug Miron, Sean Peacocke, Kathryn Parker

Bibliographic record

VenueHealthcare Quarterly · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsHolland Bloorview Kids Rehabilitation Hospital
Fundersnot available
KeywordsOrganizational cultureBest practiceWork (physics)Health carePublic relationsCulture changeSociologyBusinessEngineering ethicsManagementPolitical scienceEngineeringSocial science

Abstract

fetched live from OpenAlex

Although innovative organizations have the advantage of superior performance, the idea of adopting innovative practices and embracing risk taking at work can be intimidating, especially for those working in healthcare. When responsible for the health and safety of others, healthcare workers tend to gravitate away from ideas that could result in failure. The challenge of promoting innovation in a healthcare context can be addressed by creating an organizational culture of innovation - where innovative thinking is normalized, rewarded and even expected of employees. In this article, we share our journey and outline lessons learned in creating a culture of innovation at Holland Bloorview, Canada's largest pediatric rehabilitation hospital. It is our hope that those seeking to create a culture of innovation within their organization can learn from and apply these lessons in their own contexts.

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)0.041
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0190.024
Scholarly communication0.0300.006
Open science0.0030.022
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.189
GPT teacher head0.465
Teacher spread0.275 · 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 designQualitative
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
Published2020
Admission routes3
Has abstractyes

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