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Record W2324087148 · doi:10.1097/nor.0000000000000127

Gentle Persuasive Approaches

2015· article· en· W2324087148 on OpenAlexaff
Anne Pizzacalla, Maureen Montemuro, Esther Coker, Lori Schindel Martin, Leslie Gillies, Karen M. Robinson, Heather Pepper, Jeff Benner, Joanna Gusciora

Bibliographic record

VenueOrthopaedic Nursing · 2015
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsHealth Sciences NorthToronto Metropolitan UniversitySt. Peter's HospitalHamilton General HospitalHamilton Health Sciences
Fundersnot available
KeywordsDeliriumCurriculumDementiaUnit (ring theory)Acute careCertificationPsychologyNursingRehabilitationMedicineMedical educationPhysical therapyHealth carePsychiatryPedagogy

Abstract

fetched live from OpenAlex

Gentle Persuasive Approaches in Dementia Care (GPA), a curriculum originally designed for long-term care, was introduced into an acute care setting. This person-centered approach to supporting and responding to persons with behaviors associated with dementia was shown to be applicable for staff on an orthopaedic surgery unit where they had reported significant challenges and care burdens when faced with behaviors such as shouting, explosiveness, and resistance to care. Staff confidence in their ability to care for persons with behaviors increased after attending the 1-day GPA workshop, and they reported being highly satisfied with the curriculum, found it to be applicable to their practice, indicated that it was also useful for patients with delirium, and would recommend it to others. Some of the staff on the orthopaedic unit became certified GPA coaches. The passion of those champions, along with demonstrated success of the program on their unit, contributed to its spread to other units, including rehabilitation and acute medicine.

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)0.006
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0470.012

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 head0.123
GPT teacher head0.334
Teacher spread0.211 · 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 designNot applicable
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

Citations19
Published2015
Admission routes1
Has abstractyes

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