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Record W2793997319 · doi:10.1097/dcc.0000000000000291

Building Connections With Patients and Families in the Intensive Care Unit

2018· article· en· W2793997319 on OpenAlexafffundabout
Debbie Matchett, Michel Haddad, Jennifer Volland

Bibliographic record

VenueDimensions of Critical Care Nursing · 2018
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsCARE CanadaWestern University
FundersRegistered Nurses' Association of Ontario
KeywordsBusinessCeiling (cloud)Unit (ring theory)NursingHealth careAmbulatory careDimension (graph theory)Public relationsOperations managementMedical emergencyMedicinePsychologyPolitical science

Abstract

fetched live from OpenAlex

Consumers are increasingly becoming the voice and impetus for hospital organizational change in the United States. This is in part due to their increased stake in cost sharing with hospitals, health systems, and the ambulatory setting and revisions to health plans with higher deductibles and copays. With customers wanting services better, faster, and more economical than in the past, organizations need to break the ceiling on improvement levels for exceeding expectations of patient experience. Of interest is the hospital critical care area, because of the heightened patient needs, support, and resources that are required in this acute setting. Bluewater Health, located in Sarnia, Ontario, Canada, is a top-industry performer on the patient experience access-to-care dimension. Much can be learned from the multiple practices it has used to create an environment that embraces patients and families to the fullest extent, ensuring the resources needed for optimizing care are received.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0150.005
Scholarly communication0.0050.004
Open science0.0010.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0190.003

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 head0.073
GPT teacher head0.424
Teacher spread0.350 · 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
Published2018
Admission routes3
Has abstractyes

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