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Record W2082256708 · doi:10.3109/13561820.2014.891571

The metamorphosis of a collaborative team: from creation to operation

2014· article· en· W2082256708 on OpenAlexaff
Tracey L. Adams, Carole Orchard, Pamela E. Houghton, Rajna Ogrin

Bibliographic record

VenueJournal of Interprofessional Care · 2014
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsWestern University
Fundersnot available
KeywordsCLARITYProcess (computing)Government (linguistics)NursingTeam compositionExploratory researchTeam effectivenessQualitative researchPsychologyMedicineMedical educationKnowledge managementSociologySocial psychology

Abstract

fetched live from OpenAlex

This paper reports on the process of developing a community-based interprofessional team to provide diabetes related foot ulcer care. A new interprofessional team was formed in a local community, and the process of building a successful team was examined by the adoption of an exploratory qualitative case study approach that gathered a series of one-on-one interviews with participants at three points in time - prior to the team's formation, two months into the team's operation, and finally seven months later - shortly before the team and its clinic closed. Interviews were also conducted with a small sample of the team's patients. The factors linked to the successes and challenges of building a care team in a community setting are explored. Informants highlighted the value of regular team meetings, role clarity, and a commitment to patient-centered care. However effective collaboration was not sufficient to maintain the team in the face of poor institutional and government support.

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)0.015
metaresearch head score (Gemma)0.026
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.026
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0080.013
Scholarly communication0.0110.010
Open science0.0020.015
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.421
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

Citations25
Published2014
Admission routes1
Has abstractyes

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