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Record W2191758829 · doi:10.5430/jnep.v6n4p17

The interprofessional collaboration between nurses and speech-language pathologists working with patients diagnosed with dysphagia in skilled nursing facilities

2015· article· en· W2191758829 on OpenAlexvenueno aff
Kaitlin Dondorf, Renee Fabus, Akhtar Ebrahimi Ghassemi

Bibliographic record

VenueJournal of Nursing Education and Practice · 2015
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsDysphagiaIntervention (counseling)Speech-Language PathologyMedicineNursingHealth careSwallowingHealth professionalsPneumoniaPsychologyFamily medicinePhysical therapy

Abstract

fetched live from OpenAlex

The speech-language pathologist (SLP) is the primary person responsible for the assessment and intervention of individuals with swallowing disorders. In skilled nursing facilities, both nurses and SLPs work closely with patients diagnosed with strokes. Aspiration pneumonia is the most common cause of death in patients diagnosed with dysphagia resulting from a stroke. Due to the large number of patients with dysphagia in healthcare facilities, it is pertinent that SLP and nurses collaborate during clinical practice to improve patient outcomes. This is a discussion paper emphasizing the importance of interprofessional collaboration. Due to increasing complexity of patient care, it is important to establish collaborations early in interdisciplinary healthcare training in order to improve quality of patient care. The interdisciplinary collaboration should become a standard for training healthcare professionals including nurses and speech-language pathologists in today’s complex healthcare system.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.085
GPT teacher head0.484
Teacher spread0.398 · 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 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

Citations47
Published2015
Admission routes1
Has abstractyes

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