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Record W1602575919 · doi:10.22230/jripe.2014v4n2a152

Transdisciplinary Screening and Intervention for Nutrition, Swallowing, Cognition and Communication: A Case Study

2014· article· en· W1602575919 on OpenAlexvenueno aff
Judi Porter

Bibliographic record

VenueJournal of Research in Interprofessional Practice and Education · 2014
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
FundersMonash University
KeywordsReferralMedicineSpeech-Language PathologyIntervention (counseling)MalnutritionPsychological interventionSwallowingWorkforceScope of practiceCognitionNursingFamily medicineHealth careGerontologyPhysical therapyPathologyPsychiatry

Abstract

fetched live from OpenAlex

Background: Transdisciplinary health research and clinical practice is supported by numerous Australian health workforce documents and the broader transdisciplinary research literature. This research assessed the impact of early screening and limited intervention for nutrition, swallowing, cognition, and communication deficits in medical admissions in a large metropolitan hospital. Methods and Findings: Validated screening tools were selected and consensus for interventions were obtained by dietetic and speech pathology disciplines. Intensive training was undertaken to familiarize staff members with the screening documents and project scope. Ethics approval was obtained. Participants included 179 patients aged ≥65 years admitted to the emergency department or medical unit. The project significantly reduced referral time to both disciplines, and time to full assessment in dietetics but not speech pathology. Results found 43% of patients were malnourished or at risk of malnutrition, and 14 patients had oral intake ceased due to swallowing difficulties.Conclusions: This study has demonstrated that transdisciplinary screening and intervention may work within dietetics and speech pathology, providing an innovative extension to practice. Further alternatives using this model include the use of allied health assistants or less-experienced clinicians, while opportunities exist for transdisciplinary practices within other healthcare disciplines.

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)0.010
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.127
GPT teacher head0.599
Teacher spread0.473 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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