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Record W2185483672 · doi:10.1177/0840470414551894

Governance standards

2015· review· en· W2185483672 on OpenAlexaffabout
Jonathan I. Mitchell, Seyed Abdolmotalleb Izad Shenas, Craig Kuziemsky

Bibliographic record

VenueHealthcare Management Forum · 2015
Typereview
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of OttawaCARE Canada
Fundersnot available
KeywordsHealth careCorporate governanceAccreditationBusinessPatient safetyClinical governanceHarmOrganizational cultureQuality managementProcess managementPublic relationsMedicinePsychologyPolitical scienceService (business)MarketingMedical education

Abstract

fetched live from OpenAlex

Reducing the rate of adverse events and preventable harm associated with healthcare delivery is a policy priority across healthcare systems internationally. Care transitions or handovers in clinical care introduce risk to patients that can lead to adverse events. Building on the literature showing the impact of governing boards on the quality and safety of healthcare services, this study builds a predictive model between governance and safety at care transitions. Analysis was based on 490 Canadian healthcare organizations across six sectors of care. Organizational compliance with the Accreditation Canada Governance Standards was shown to be a predictor of organizational performance with the Medication Reconciliation Required Organizational Practice. Results indicate that among a comprehensive set of governance standards, a set of 11 central governance elements predict organizational medication reconciliation practice. Implications for healthcare leaders are discussed including the role of organizational leadership in medication reconciliation practices. The importance of a governance process cycle that encompasses board prioritization of desired goals, monitoring of performance regularly, and communicating results with stakeholders is shown.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.742
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.176
GPT teacher head0.528
Teacher spread0.352 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2015
Admission routes2
Has abstractyes

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