MétaCan
Menu
Back to cohort
Record W2743321148 · doi:10.1177/1363459317724852

Understanding the emergence and development of medical collaboration across organizational boundaries: A longitudinal case study

2017· article· en· W2743321148 on OpenAlexafffundabout
Nassera Touati, Charo Rodríguez, Marie-Andrée Paquette, Jean‐Louis Denis

Bibliographic record

VenueHealth An Interdisciplinary Journal for the Social Study of Health Illness and Medicine · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversité de MontréalUniversité de SherbrookeMcGill UniversityÉcole Nationale d'Administration Publique
FundersCanadian Institutes of Health Research
KeywordsLongitudinal studyKnowledge managementSociologyMedicineComputer science

Abstract

fetched live from OpenAlex

Our goal in this investigation was to help shed light on the very difficult process of collaboration between family physicians and specialists working at different levels of healthcare delivery. More precisely, and grounded on Giddens' structuration theory, our investigation aims to understand how medical collaboration emerges and develops around chronic patients. This was a longitudinal interpretive case study, the "case" being a continuum-of-care for patients suffering from diabetes, put in place in an urban health center in the Canadian province of Quebec. The study shows how the application of rules of signification and of legitimation, combined with domination resources, have supported the emergence of new forms of collaborative practices. Our analysis reveals, however, that new collaborative practices at the administrative level do not necessarily entail greater shared decision-making in patient management and the mobilization of knowledge across boundaries. The study also corroborates the mutual recursive influence of practices and structures. Our study's most important contribution concerns the impact of knowledge dynamics, that is, individual and collective learning, on the development of medical collaboration across levels of care.

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)0.024
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.1190.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.652
GPT teacher head0.700
Teacher spread0.048 · 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

Citations10
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueHealth An Interdisciplinary Journal for the Social Study of Health Illness and MedicineSame topicHealth Policy Implementation ScienceFrench-language works237,207