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Record W1978250527 · doi:10.3109/13561820.2014.917614

A multi-dimensional analysis of person-centred collaborative practice for designing an educational curriculum in continuing care centres

2014· article· en· W1978250527 on OpenAlexaff
Irene Coulson, Shirley Galenza, Sharon Bratt, Colette Foisy‐Doll, Mary Haase

Bibliographic record

VenueJournal of Interprofessional Care · 2014
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMacEwan University
FundersWorld Health Organization
KeywordsTeamworkCurriculumHealth carePlan (archaeology)Medical educationSociologyNursingPsychologyPublic relationsKnowledge managementPedagogyMedicineManagementPolitical scienceComputer science

Abstract

fetched live from OpenAlex

A significant transformation occurring in the continuing care industry is an attempt to shift the culture from impersonal institutions into true person-centred care (PCC) homes. This approach re-orients the facility's values, attitudes, norms and hierarchies while creating flexible role descriptions to promote collaborative teamwork. PCC practices will require healthcare teams to develop new approaches that empower residents and families to become partners in the development of a plan of care. This report outlines a study, which will gather data from an organizational policy analysis and interviews with residents and healthcare staff. These data will be examined through a sociological lens to identify areas for team improvement. The results will guide the design of a training curriculum to be delivered using traditional and multi-modal hi-fidelity simulation methods.

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)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0030.004
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.435
Teacher spread0.409 · 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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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