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Record W6963463419 · doi:10.20381/ruor-9819

A catalyst for system change: a case study of child health network formation, evolution and sustainability in Canada

2017· other· en· W6963463419 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Ottawa - Library · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityPublic healthFocus groupSocial network analysisQualitative researchService (business)Service delivery frameworkQualitative propertyChild health

Abstract

fetched live from OpenAlex

Abstract Background The purpose of this study was to examine key processes and supportive and inhibiting factors involved in the development, evolution, and sustainability of a child health network in rural Canada. This study contributes to a relatively new research agenda aimed at understanding inter-organizational and cross-sectoral health networks. These networks encourage collaboration focusing on complex issues impacting health – issues that individual agencies cannot effectively address alone. This paper presents an overview of the study findings. Methods An explanatory qualitative case study approach examined the Network's 13-year lifespan. Data sources were documents and Network members, including regional and 71 provincial senior managers from 11 child and youth service sectors. Data were collected through 34 individual interviews and a review of 127 documents. Interview data were analyzed using framework analysis methods; Prior's approach guided document analysis. Results Three themes related to network development, evolution and sustainability were identified: (a) Network relationships as system triggers, (b) Network-mediated system responsiveness, and (c) Network practice as political. Conclusions Study findings have important implications for network organizational development, collaborative practice, interprofessional education, public policy, and public system responsiveness research. Findings suggest it is important to explicitly focus on relationships and multi-level socio-political contexts, such as supportive policy environments, in understanding health networks. The dynamic interplay among the Network members; central supportive and inhibiting factors; and micro-, meso-, and macro-organizational contexts was identified.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.204
Teacher spread0.194 · 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 designNot applicable
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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