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Record W2150390434 · doi:10.1080/13561820310001608195

Building empowering partnerships for interprofessional care

2003· article· en· W2150390434 on OpenAlexaffabout
Carol L. McWilliam, Sandra B. Coleman, Catherine Melito, Donnabeth Sweetland, John Saidak, Jennifer Smit, Tracey Thompson, Gordon Milak

Bibliographic record

VenueJournal of Interprofessional Care · 2003
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsVictorian Order of NursesHome and Community Care Support ServicesWestern University
FundersRoyal Society
KeywordsGeneral partnershipContext (archaeology)Public relationsHealth careKnowledge managementBusinessQuality (philosophy)NursingMedicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

While partnership approaches have the potential to achieve cost-effective quality health care, several attributes of the current context make partnerships difficult to achieve. This paper provides an analysis of the socio-cultural, structural and human challenges to building partnerships at both personal and organizational levels, together with an empowering interdisciplinary approach for overcoming these barriers. Premised on empirical evidence, 'flexible client-driven care', currently being tested in the home care sector in Canada, encompasses structures and processes that promote relationship-building and conscientious critical application of individual and collective potential for achieving health care. Strategies for implementing empowering partnership-building at both personal and organizational levels are elaborated, together with the challenges encountered. The practical issues addressed afford insights and ideas for others who may be attempting to achieve similar partnership aims.

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)0.021
metaresearch head score (Gemma)0.027
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 score0.021
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0100.008
Scholarly communication0.0090.011
Open science0.0020.032
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.057
GPT teacher head0.486
Teacher spread0.429 · 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

Citations36
Published2003
Admission routes2
Has abstractyes

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