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Record W1966156093 · doi:10.3109/13561820.2014.909795

Shifting contours of boundaries: an exploration of inter-agency integration between hospital and community interprofessional diabetes programs

2014· article· en· W1966156093 on OpenAlexafffundabout
René Wong, Petra Breiner, Maria Mylopoulos

Bibliographic record

VenueJournal of Interprofessional Care · 2014
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsSickKids FoundationUniversity of Toronto
FundersAssociation of Faculties of Medicine of Canada
KeywordsAgency (philosophy)BureaucracyFacilitatorGrounded theoryPower (physics)Constructivist grounded theorySociologyPublic relationsProfessional boundariesProcess (computing)NursingPsychologyMedicinePolitical scienceQualitative researchSocial psychologySocial sciencePolitics

Abstract

fetched live from OpenAlex

This article reports on research into the relationships that emerged between hospital-based and community-based interprofessional diabetes programs involved in inter-agency care. Using constructivist grounded theory methodology we interviewed a purposive theoretical sample of 21 clinicians and administrators from both types of programs. Emergent themes were identified through a process of constant comparative analysis. Initial boundaries were constructed based on contrasts in beliefs, practices and expertise. In response to bureaucratic and social pressures, boundaries were redefined in a way that created role uncertainty and disempowered community programs, ultimately preventing collaboration. We illustrate the dynamic and multi-dimensional nature of social and symbolic boundaries in inter-agency diabetes care and the tacit ways in which hospitals can maintain a power position at the expense of other actors in the field. As efforts continue in Canada and elsewhere to move knowledge and resources into community sectors, we highlight the importance of hospitals seeing beyond their own interests and adopting more altruistic models of inter-agency integration.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.003
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 head0.066
GPT teacher head0.421
Teacher spread0.355 · 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

Citations2
Published2014
Admission routes3
Has abstractyes

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