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Record W2168029400 · doi:10.3109/13561820.2012.719943

Key trends in interprofessional research: A macrosociological analysis from 1970 to 2010

2012· article· en· W2168029400 on OpenAlexaff
Elise Paradis, Scott Reeves

Bibliographic record

VenueJournal of Interprofessional Care · 2012
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsField (mathematics)LegitimacyInterprofessional educationThematic analysisSociologyRhetoricData scienceSocial sciencePsychologyQualitative researchPolitical scienceComputer scienceHealth careLawPolitics

Abstract

fetched live from OpenAlex

The field of interprofessional research has grown both in size and in importance since the 1970s. In this paper, we use a macrosociological approach and a Bourdieusian theoretical framework to investigate this growth and the changing nature of the field's research. We investigate publication trends at the aggregate (field) level, using an original dataset of 100,488 interprofessional-related articles published between 1970 and 2010 and recorded in the PubMed database. Articles were coded using a list of 638 codes that were then analyzed thematically and longitudinally. Our results are presented in two main sections. Initially, we consider the growth and reach of the interprofessional field. Second, we explore the five different trends ("terminological issues", "rising management rhetoric", "expansion of psychometrics", "shift from individualism to collectivism" and "emerging issues") that emerged out of our thematic analysis of publications over time. These findings are discussed in the light of Bourdieu's framework to provide an indication of what we argue is a growing legitimacy of the field of interprofessional research as a scholarly domain in its own right.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0120.001

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 head0.132
GPT teacher head0.551
Teacher spread0.419 · 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 designObservational
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

Citations60
Published2012
Admission routes1
Has abstractyes

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