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Record W1138306048 · doi:10.1159/000381204

Frailty and Interprofessional Collaboration

2015· review· en· W1138306048 on OpenAlexaff
Marion Briggs, Janet E. McElhaney

Bibliographic record

VenueInterdisciplinary topics in gerontology and geriatrics · 2015
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsHealth Sciences NorthNOSM University
Fundersnot available
KeywordsPsychological interventionInterdependenceContext (archaeology)ImprovisationNursingHealth careMedicineBest practicePsychologyMedical educationSociology

Abstract

fetched live from OpenAlex

This chapter underscores the importance of interprofessional collaboration in the care of frail older patients. Hospital-based care is emphasized because interprofessionalism is difficult in that setting since the setting is constantly changing and since multiple healthcare professionals care for many complex, very ill patients, only some of whom are frail older people. Interprofessionalism is particularly important and challenging in teaching units in the acute care setting, where many health professionals practice and learn together and team membership changes frequently. Learning is enhanced and interprofessionalism can enhance learning by viewing the patient as a key part of the teaching team. While 'best practice' interventions have been identified for frail older adults who are hospitalized, these interventions are not easily implemented in routine hospital care. Three interdependent processes in clinical practice--representation, sense-making, and improvisation--are described, which contribute to an understanding of how practices change when implemented in a way that takes the local context into account and keeps person-centered care as the central consideration.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.002
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 head0.113
GPT teacher head0.529
Teacher spread0.416 · 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.

Study designNot applicable
Domainnot available
GenreReview

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
Published2015
Admission routes1
Has abstractyes

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