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Record W3121076245 · doi:10.3390/healthcare9020146

Implementation of the Family HELP Protocol: A Feasibility Project for a West Texas ICU

2021· article· en· W3121076245 on OpenAlexaboutno aff
Rebecca McClay

Bibliographic record

VenueHealthcare · 2021
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsProtocol (science)NursingSummative assessmentMedicineIntervention (counseling)DeliriumIntensive care unitUnit (ring theory)PsychologyFamily medicineAlternative medicineFormative assessmentPsychiatry

Abstract

fetched live from OpenAlex

The purpose of this project was to determine if bedside intensive care unit (ICU) nurse buy-in to the Family Hospital Elder Life Program (HELP) protocol was sufficient to make implementation feasible at one county hospital in West Texas. Surveys were anonymous with ballot box collection being available to the bedside ICU nurses for one week each. Questions were based on literature findings of expected outcomes, identified barriers and facilitators, Calgary Family Intervention Method framework domains, and the Centers for Disease Control and Prevention Framework for program evaluation. Outcome measures were taken from the stated aims of the project and evaluated from paired baseline and summative survey questions. Survey participation was approximately half of nurses employed in the studied ICU. Analysis of the surveys showed a positive perception of family presence decreasing patient delirium symptoms, and a positive perception of the Family HELP protocol. The results described a high perception of family members as partners in care and high intention to implement the Family HELP protocol, indicating strong support of a full implementation of the protocol. The high level of bedside nurse buy-in present in this study has large implications for successful implementation of the Family HELP protocol in the near future, with sustainability and continued use supported by potential inclusion of the task in the electronic health record charting.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.094
GPT teacher head0.447
Teacher spread0.353 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2021
Admission routes1
Has abstractyes

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