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Record W2153781202 · doi:10.1186/1472-6963-8-188

A model and typology of collaboration between professionals in healthcare organizations

2008· article· en· W2153781202 on OpenAlexafffundabout
Danielle D’Amour, Lise Goulet, Jean-François Labadie, Leticia San Martín‐Rodríguez, Raynald Pineault

Bibliographic record

VenueBMC Health Services Research · 2008
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsCentre Intégré de Santé et de Services Sociaux des LaurentidesUniversité de Montréal
FundersMinistère de la SantéMinistère de la Santé et des Services sociauxCanadian Health Services Research Foundation
KeywordsTypologyHealth administrationHealth careNursing researchHealth informaticsKnowledge managementDimension (graph theory)MedicineNursingProcess managementPublic relationsBusinessSociologyPublic healthComputer sciencePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The new forms of organization of healthcare services entail the development of new clinical practices that are grounded in collaboration. Despite recent advances in research on the subject of collaboration, there is still a need for a better understanding of collaborative processes and for conceptual tools to help healthcare professionals develop collaboration amongst themselves in complex systems. This study draws on D'Amour's structuration model of collaboration to analyze healthcare facilities offering perinatal services in four health regions in the province of Quebec. The objectives are to: 1) validate the indicators of the structuration model of collaboration; 2) evaluate interprofessional and interorganizational collaboration in four health regions; and 3) propose a typology of collaboration METHODS: A multiple-case research strategy was used. The cases were the healthcare facilities that offer perinatal services in four health regions in the province of Quebec (Canada). The data were collected through 33 semi-structured interviews with healthcare managers and professionals working in the four regions. Written material was also analyzed. The data were subjected to a "mixed" inductive-deductive analysis conducted in two main stages: an internal analysis of each case followed by a cross-sectional analysis of all the cases. RESULTS: The collaboration indicators were shown to be valid, although some changes were made to three of them. Analysis of the data showed great variation in the level of collaboration between the cases and on each dimension. The results suggest a three-level typology of collaboration based on the ten indicators: active collaboration, developing collaboration and potential collaboration. CONCLUSION: The model and the typology make it possible to analyze collaboration and identify areas for improvement. Researchers can use the indicators to determine the intensity of collaboration and link it to clinical outcomes. Professionals and administrators can use the model to perform a diagnostic of collaboration and implement interventions to intensify it.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.007
Science and technology studies0.0070.022
Scholarly communication0.0140.017
Open science0.0030.008
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.127
GPT teacher head0.566
Teacher spread0.438 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations534
Published2008
Admission routes3
Has abstractyes

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