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Record W2089831158 · doi:10.1080/13561820802380035

More than the sum of its parts? A qualitative research synthesis on multi-disciplinary primary care teams

2008· review· en· W2089831158 on OpenAlexaff
Emmanuelle Bélanger, Charo Rodríguez

Bibliographic record

VenueJournal of Interprofessional Care · 2008
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMcGill University
Fundersnot available
KeywordsQualitative researchCitationDisciplineBibliographic databasePrimary carePsychologyMEDLINEKnowledge managementMedical educationSociologyMedicineComputer scienceLibrary sciencePolitical scienceSocial scienceFamily medicine

Abstract

fetched live from OpenAlex

This qualitative research synthesis reviews interpretive scholarly papers on multi-disciplinary primary care teams. A bibliographic search was conducted in electronic databases: Medline, Embase, and the Web of Science Citation Index, and in the references of retrieved papers. The research consists of a taxonomic analysis of 19 qualitative studies about primary care teams published in peer-reviewed journals between 2001 and July 2008 in English and French. Nineteen qualitative studies were synthesized. Two major concerns emerged: (1) strategies for organizational change toward effective co-operative practice, and (2) dimensions of team interactions and work relations. The authors conclude that qualitative results suggest common strategies to improve the development of primary care teams, while identifying dimensions of team interactions that remain problematic. A fundamental aspect of team formation appears to be overlooked, i.e., the construction of a collective identity, which would involve the whole team in a shared ideal of co-operative practice. The adoption of discourse analysis is suggested as a more sophisticated qualitative methodology to explore this issue.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.196
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.007
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 head0.240
GPT teacher head0.615
Teacher spread0.375 · 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 designQualitative
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

Citations84
Published2008
Admission routes1
Has abstractyes

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