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Development of interprofessional collaborative practices within undergraduate programs on healthcare: case study on the Family Health Alliance in Fortaleza (Ceará, Brazil)

2011· article· en· W2064305473 on OpenAlexaff
Ivana Cristina de Holanda Cunha Barrêto, Francisco Antônio Loiola, Luiz Odorico Monteiro de Andrade, Ana Ester Maria Melo Moreira, Caio Garcia Correia de Sá Cavalcanti, Carlos André Moura Arruda, André Luiz Façanha da Silva

Bibliographic record

VenueInterface - Comunicação Saúde Educação · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsThematic analysisAllianceEnthusiasmContext (archaeology)Interprofessional educationMedical educationExploratory researchHealth carePerspective (graphical)PsychologyFamily healthQualitative researchSociologyNursingMedicinePolitical science

Abstract

fetched live from OpenAlex

The authors present the dynamics of mentoring work within the Family Health Alliance (FHA), an extension program that fosters interprofessional collaboration from a social learning perspective. The objective of this study was to describe and evaluate the program dynamics and their repercussions among the participants. The context, basis, objectives and organization of work situations for students from six different undergraduate programs on health sciences were analyzed by means of an exploratory development study conducted between January and December 2008. In the first phase, the participants were two teams consisting of one mentor and four students. In the second phase, there were three mentors and eight students. Thematic analysis on the students discourse emphasized their enthusiasm about the possibility of interprofessional collaboration as an instrument for change. The professional mentors were found to have developed a better understanding of their role and greater teaching awareness.

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)0.006
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
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 head0.280
GPT teacher head0.514
Teacher spread0.235 · 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
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

Citations9
Published2011
Admission routes1
Has abstractyes

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