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Record W1991033957 · doi:10.5430/jha.v3n4p25

The impact of acute care clinical practice guidelines on length of stay: A closer look at some conflicting findings

2014· article· en· W1991033957 on OpenAlexaffvenueabout
Moriah Ellen, G. Ross Baker, Adalsteinn Brown

Bibliographic record

VenueJournal of Hospital Administration · 2014
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsUniversity of TorontoMcMaster University
Fundersnot available
KeywordsCompliance (psychology)Health careNursingMedicineVariety (cybernetics)Resistance (ecology)Health professionalsOrganizational structureClinical PracticePsychologyFamily medicinePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Systematic reviews have found that clinical practice guidelines (CPGs) are associated with lower lengths of stay (LOS), but a secondary analysis of Ontario acute care hospitals found few significant relationships between CPGs and LOS. This research explored possible reasons for these findings and what other factors may impact the CPG-LOS relationship. Semi-structured interviews were conducted with staff from nine hospitals whose jobs dealt with developing, implementing, monitoring, updating, or evaluating CPGs. Interviews were analyzed utilizing methods outlined by Aurebach. A variety of leaders and hospital types were represented. Five main factors influencing relationships between CPGs and LOS were identified: 1) the purpose of implementation, 2) evidence base for CPG content and selection, 3) health care professionals’ response to change and compliance, 4) dissemination strategies, and 5) organizational support and resources. The interviews suggested possible reasons why CPGs are not realizing their full potential impact on LOS in Ontario hospitals, ranging from poor compliance to resistance from health care providers. CPGs themselves are not perceived to be the reason for ineffectiveness; rather, organizational- and individual-level barriers seem to be the causes.

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)0.004
metaresearch head score (Gemma)0.038
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
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 head0.119
GPT teacher head0.538
Teacher spread0.419 · 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 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

Citations4
Published2014
Admission routes3
Has abstractyes

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